oru.sePublications
Change search
Refine search result
1 - 9 of 9
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 1.
    Alam, Md. Moudud
    Örebro University, Swedish Business School at Örebro University.
    Feasible computation of generalized linear mixed models with application to credit risk modelling2010Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    This thesis deals with developing and testing feasible computational procedures to facilitate the estimation of and carry out the prediction with the generalized linear mixed model (GLMM) with a scope of applying them to large data sets. The work of this thesis is motivated from an issue arising incredit risk modelling. We have access to a huge data set, consisting of about one million observations, on credit history obtained from two major Swedish banks. The principal research interest involved with the data analysis is to model the probability of credit defaults by incorporating the systematic dependencies among the default events. In order to model the dependent credit defaults we adopt the framework of GLMM which is apopular approach to model correlated binary data. However, existing computational procedures for GLMM did not offer us the flexibility to incorporate the desired correlation structure of defaults events.For the feasible estimation of the GLMM we propose two estimation techniques being the fixed effects (FE) approach and the two-step pseudolikelihood approach (2PL). The preciseness of the estimation techniques and their computational advantages are studied by Monte-Carlo simulations and by applying them to the credit risk modelling. Regarding the prediction issue, we show how to apply the likelihood principle to carryout prediction with GLMM. We also provide an R add-in package to facilitate the predictive inference for GLMM.

    List of papers
    1. Computationally feasible estimation of the covariance structure in generalized linear mixed models 
    Open this publication in new window or tab >>Computationally feasible estimation of the covariance structure in generalized linear mixed models 
    2008 (English)In: Journal of Statistical Computation and Simulation, ISSN 0094-9655, E-ISSN 1563-5163, Vol. 78, no 12, p. 1229-1239Article in journal (Refereed) Published
    Abstract [en]

    In this paper, we discuss how a regression model, with a non-continuous response variable, which allows for dependency between observations, should be estimated when observations are clustered and measurements on the subjects are repeated. The cluster sizes are assumed to be large.We find that the conventional estimation technique suggested by the literature on generalized linear mixed models(GLMM) is slow and sometimes fails due to non-convergence and lack of memory on standard PCs.We suggest to estimate the random effects as fixed effects by generalized linear model and to derive the covariance matrix from these estimates.A simulation study shows that our proposal is feasible in terms of mean-square error and computation time.We recommend that our proposal be implemented in the software of GLMM techniques so that the estimation procedure can switch between the conventional technique and our proposal, depending on the size of the clusters.

    Place, publisher, year, edition, pages
    London: Taylor & Francis, 2008
    Keywords
    Monte Carlo simulations, Large sample, Interdependence, Cluster errors
    National Category
    Probability Theory and Statistics Social Sciences
    Research subject
    Statistics
    Identifiers
    urn:nbn:se:oru:diva-14060 (URN)10.1080/00949650701688547 (DOI)
    Note
    Mr Alam is also affiliated to Dalarna University, SE 781 88 Borlange, SwedenAvailable from: 2011-01-19 Created: 2011-01-19 Last updated: 2017-12-11Bibliographically approved
    2. Feasible estimation of generalized linear mixed models (GLMM) with weak dependency between groups
    Open this publication in new window or tab >>Feasible estimation of generalized linear mixed models (GLMM) with weak dependency between groups
    2010 (English)Manuscript (preprint) (Other academic)
    Abstract [en]

    This paper presents a two-step pseudo likelihood estimation for generalized linear mixed models with the random effects being correlated between groups. The core idea is to deal with the random intractable integrals in  the likelihood function by multivariate Taylor's approximation. The accuracy of the estimation technique is assessed in a Monte-Carlo study: An application of it with binary response variable is presented using a real dara set on credit defaults from two Swedish banks. Thanks to   the use of two-step estimation technique, the proposed algorithm outperforms conventional likelihood algoritms in terms of computational time.

    Keywords
    PQL, Laplace approximation, interdependence, cluster errrors, credit risk model
    National Category
    Social Sciences Probability Theory and Statistics
    Research subject
    Statistics
    Identifiers
    urn:nbn:se:oru:diva-14061 (URN)
    Note

    Mr Alam is also affiliated to Dalarna University, SE 781 88 Borlange, Sweden

    Available from: 2011-01-19 Created: 2011-01-19 Last updated: 2017-10-17Bibliographically approved
    3. Industry shocks and empirical evidences on defaults comovements
    Open this publication in new window or tab >>Industry shocks and empirical evidences on defaults comovements
    (English)Manuscript (preprint) (Other academic)
    Abstract [en]

    It is commonly agreed that the credit defaults are correlated. However, the structure and magnitude of such dependence is not yet fully understood. This paper contributes to the current understanding about the defaults comovement in the following way. Assuming that the industries provides the basis of defaults comovement it provides empirical evidence as to how such comovements can be modeled using correlated industry shocks. Generalized linear mixed model (GLMM) with correlated random effects is used to model the defaults comovement. It is also demonstrated as to how a GLMM with complex correlation structure can be estimated through a very simple way. Empirical evidences are drawn through analyzing quarterly individual borrower level credit history data obtained from two major Swedish banks between the period 1994 and 2000. The results show that, conditional on the borrower level accounting data and macro business cycle variables, the defaults are correlated both within and between industries but not over time (quarters). A discussion has also been presented as to how a GLMM for defaults correlation can be explained.

    Keywords
    Credit risk, defaults contagion, GLMM, cluster correlation
    National Category
    Social Sciences Probability Theory and Statistics
    Research subject
    Statistics
    Identifiers
    urn:nbn:se:oru:diva-14072 (URN)
    Note

    Mr Alam is also affiliated to Dalarna University, SE 781 88 Borlange, Sweden

    Available from: 2011-01-19 Created: 2011-01-19 Last updated: 2017-10-17Bibliographically approved
    4. Likelihood prediction for generalized linear mixed models under covariate uncertainty
    Open this publication in new window or tab >>Likelihood prediction for generalized linear mixed models under covariate uncertainty
    2010 (English)Manuscript (preprint) (Other academic)
    Abstract [en]

    This paper presents the techniques of likelihood prediction for the generalized linear mixed models. Methods of likelihood prediction is explained through a series of examples; from a classical one to more complicated ones. The examples show, in simple cases, that the likelihood prediction (LP) coincides with already known best frequentist practice such as the best linear unbiased predictor. The paper outlines a way to deal with the covariate uncertainty while producing predictive inference. Using a Poisson error-in-variable general-ized linear model, it has been shown that in complicated cases LP produces better results than already know methods.

    Keywords
    Predictive likelihood, Pro…le predictive likelihood, Stochastic covariate, Coverage interval, Future value prediction, Credit risk prediction
    National Category
    Social Sciences Probability Theory and Statistics
    Research subject
    Statistics
    Identifiers
    urn:nbn:se:oru:diva-14079 (URN)
    Note

    Mr Alam is also affiliated to Dalarna University, SE 781 88 Borlange, Sweden

    Available from: 2011-01-19 Created: 2011-01-19 Last updated: 2017-10-17Bibliographically approved
    5. Computation and application of likelihood prediction with generalized linear and mixed models
    Open this publication in new window or tab >>Computation and application of likelihood prediction with generalized linear and mixed models
    (English)Manuscript (preprint) (Other academic)
    Abstract [en]

    This paper presents the computation of likelihood prediction with the generalized linear and mixed models. The method of likelihood prediction is briefy discussed and approximate formulae are provided to make easy computation of the likelihoodprediction with generalized linear models. For complicated prediction problems, simulation methods are suggested. An R add-in package is accompanied to carryout the computation of the predictive inference with the generalized linear and mixed models. The likelihood prediction is applied to the prediction of the credit defaults using a real data set. Results show that the predictive likelihood can be a useful tool to predict portfolio credit risk.

    Keywords
    Predictive likelihood, Pro…le predictive likelihood, Coverage inter- val, Future value prediction, Credit risk prediction, R-package.
    National Category
    Social Sciences Probability Theory and Statistics
    Research subject
    Statistics
    Identifiers
    urn:nbn:se:oru:diva-14081 (URN)
    Note

    Mr Alam is also affiliated to Dalarna University, SE 781 88 Borlange, Sweden

    Available from: 2011-01-19 Created: 2011-01-19 Last updated: 2017-10-17Bibliographically approved
  • 2.
    Ding, Shutong
    Örebro University, Örebro University School of Business.
    Model choice in Bayesian VAR models2014Doctoral thesis, comprehensive summary (Other academic)
    List of papers
    1. Model averaging and variable selection in VAR models
    Open this publication in new window or tab >>Model averaging and variable selection in VAR models
    (English)Manuscript (preprint) (Other academic)
    Abstract [en]

    Bayesian model averaging and model selection is based on the marginal likelihoods of the competing models. This can, however, not be used directly in VAR models when one of the issues is which - and how many - variables to include in the model since the likelihoods will be for different groups of variables and not directly comparable. One possible solution is to consider the marginal likelihood for a core subset of variables that are always included in the model. This is similar in spirit to a recent proposal for forecast combination based on the predictive likelihood. The two approaches are contrasted and their performance is evaluated in a simulation study and a forecasting exercise. 

    Keywords
    Bayesian model averaging, marginalized likelihood, predictive likelihood
    National Category
    Probability Theory and Statistics
    Research subject
    Statistics
    Identifiers
    urn:nbn:se:oru:diva-35873 (URN)
    Available from: 2014-08-07 Created: 2014-08-07 Last updated: 2017-10-17Bibliographically approved
    2. Bayesian forecasting combination in VAR models with many predictors
    Open this publication in new window or tab >>Bayesian forecasting combination in VAR models with many predictors
    (English)Manuscript (preprint) (Other academic)
    Abstract [en]

    This paper is motivated by the findings of our previous work, that is forecasting VAR models in the cases of small and medium-sized datasets, both marginalized marginal likelihood and predictive likelihood based averaging approaches tend to produce superior forecasts than the Bayesian VAR methods using shrinkage priors. With an efficient reversible-jump MCMC algorithm, We extend the forecast combination and model averaging of VAR models to the context of large datasets (more than hundred predictors), and consider a range of competitive alternative methods to compare and examine their forecast performance. Our empirical results show that the Bayesian model averaging approach outperforms the various alternatives.

    Keywords
    Bayesian model averaging, large datasets, marginalized marginal likelihood, reversible-jump MCMC
    National Category
    Probability Theory and Statistics
    Research subject
    Statistics
    Identifiers
    urn:nbn:se:oru:diva-35874 (URN)
    Available from: 2014-08-07 Created: 2014-08-07 Last updated: 2017-10-17Bibliographically approved
    3. Bayesian forecasting using reduced rank VARs
    Open this publication in new window or tab >>Bayesian forecasting using reduced rank VARs
    (English)Manuscript (preprint) (Other academic)
    Abstract [en]

    Reduced rank regression has a long tradition as a technique to achieve a parsimonious parameterization in multivariate regression models. Recently this has been applied in the Bayesian VAR framework where the rich parameterization is a common concern in applied work. We advocate a parameterization of the reduced rank VAR which leads to a natural interpretation in terms of a dynamic factor model. Without additional restrictions on the parameters the reduced rank model is unidentified and we consider two identification schemes. The traditional ad-hoc identification with the first rows of one of the reduced rank parameter matrices being the identity matrix and a semi-orthogonal identification originally proposed in the context of cointegrated VAR models with the advantage that it does not depend on the ordering of the variables. Borrowing from the cointegration literature, we propose efficient MCMC algorithms for the evaluation of the posterior distribution given the two identification schemes. The determination of the rank of the reduced rank VAR is an important practical issue and we study the performance of different criteria for determining the rank. Finally, the forecasting performance of the reduced rank VAR model is evaluated in comparison with other popular forecasting models for large data sets.

    Keywords
    Model selection, Bayesian VAR model, Reduced rank regression, Markov chain Monte Carlo
    National Category
    Probability Theory and Statistics
    Research subject
    Statistics
    Identifiers
    urn:nbn:se:oru:diva-35875 (URN)
    Available from: 2014-08-07 Created: 2014-08-07 Last updated: 2017-10-17Bibliographically approved
    4. Bayesian VAR models with asymmetric lags
    Open this publication in new window or tab >>Bayesian VAR models with asymmetric lags
    (English)Manuscript (preprint) (Other academic)
    Abstract [en]

    Most studies estimate the VAR models with equal lag length. Little attention has been paid to the issue of lag specifications. In this paper we propose VAR models with asymmetric lags via Bayesian sparse learning. Three popular sparse priors, L1-penalized Lasso, the mixture of L1 and L2 penalties elastic net, and spike and slab type are developed using hierarchical Bayes formulation. The model identification performance is assessed with Monte Carlo experiment and the forecasting performance is evaluated with US macroeconomic data.

    Keywords
    Bayesian shrinkage, vector autoregression, sparsity, Lasso, elastic net, spike and slab prior, asymmetric lags
    National Category
    Probability Theory and Statistics
    Research subject
    Statistics
    Identifiers
    urn:nbn:se:oru:diva-35876 (URN)
    Available from: 2014-08-07 Created: 2014-08-07 Last updated: 2017-10-17Bibliographically approved
  • 3.
    Högberg, Hans
    Örebro University, Swedish Business School at Örebro University.
    Some properties of measures of disagreement and disorder in paired ordinal data2010Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    The measures studied in this thesis were a measure of disorder, D, and a measure of the individual part of the disagreement, the measure of relative rank variance, RV, proposed by Svensson in 1993. The measure of disorder is a useful measure of order consistency in paired assessments of scales with a different number of possible values. The measure of relative rank variance is a useful measure in evaluating reliability and for evaluating change in qualitative outcome variables.

    In Paper I an overview of methods used in the analysis of dependent ordinal data and a comparison of the methods regarding the assumptions, specifications, applicability, and implications for use were made. In Paper II an application, and a comparison of the results of some standard models, tests, and measures to two different research problems were made. The sampling distribution of the measure of disorder was studied both analytically and by a simulation experiment in Paper III. The asymptotic normal distribution was shown by the theory of U-statistics and the simulation experiments for finite sample sizes and various amount of disorder showed that the sampling distribution was approximately normal for sample sizes of about 40 to 60 for moderate sizes of D and for smaller sample sizes for substantial sizes of D. The sampling distribution of the relative rank variance was studied in a simulation experiment in Paper IV. The simulation experiment showed that the sampling distribution was approximately normal for sample sizes of 60-100 for moderate size of RV, and for smaller sample sizes for substantial size of RV. In Paper V a procedure for inference regarding relative rank variances from two or more samples was proposed. Pair-wise comparison by jackknife technique for variance estimation and the use of normal distribution as approximation in inference for parameters in independent samples based on the results in Paper IV were demonstrated. Moreover, an application of Kruskal-Wallis test for independent samples and Friedman’s test for dependent samples were conducted.

    List of papers
    1. An overview of methods in the analysis of dependent ordered categorical data: assumptions and implications
    Open this publication in new window or tab >>An overview of methods in the analysis of dependent ordered categorical data: assumptions and implications
    2008 (English)Report (Other academic)
    Abstract [en]

    Subjective assessments of pain, quality of life, ability etc. measured by rating scales and questionnaires are common in clinical research. The resulting responses are categorical with an ordered structure and the statistical methods must take account of this type of data structure. In this paper we give an overview of methods for analysis of dependent ordered categorical data and a comparison of standard models and measures with nonparametric augmented rank measures proposed by Svensson. We focus on assumptions and issues behind model specifications and data as well as implications of the methods. First we summarise some fundamental models for categorical data and two main approaches for repeated ordinal data; marginal and cluster-specific models. We then describe models and measures for application in agreement studies and finally give a summary of the approach of Svensson. The paper concludes with a summary of important aspects.

    Place, publisher, year, edition, pages
    Örebro: Örebro universitet, 2008. p. 33
    Series
    Working paper series, Swedish business school at Örebro, ISSN 1403-0586 ; 7
    National Category
    Probability Theory and Statistics
    Research subject
    Statistics
    Identifiers
    urn:nbn:se:oru:diva-12664 (URN)
    Available from: 2010-12-06 Created: 2010-12-06 Last updated: 2017-10-17Bibliographically approved
    2. Comparison of methods in the analysis of dependent ordered catagorical data
    Open this publication in new window or tab >>Comparison of methods in the analysis of dependent ordered catagorical data
    2008 (English)Report (Other academic)
    Abstract [en]

    Rating scales for outcome variables produce categorical data which are often ordered and measurements from rating scales are not standardized. The purpose of this study is to apply commonly used and novel methods for paired ordered categorical data to two data sets with different properties and to compare the results and the conditions for use of these models.

    The two applications consist of a data set of inter-rater reliability and a data set from a follow-up evaluation of patients. Standard measures of agreement and measures of association are used. Various loglinear models for paired categorical data using properties of quasi-independence and quasi-symmetry as well as logit models with a marginal modelling approach are used. A nonparametric method for ranking and analyzing paired ordered categorical data is also used.

    We show that a deeper insight when it comes to disagreement and change patterns may be reached using the nonparametric method and illustrate some problems with standard measures as well as parametric loglinear and logit models. In addition, the merits of the nonparametric method are illustrated.

    Place, publisher, year, edition, pages
    Örebro: Örebro university, 2008. p. 19
    Series
    Working paper series, Swedish business school at Örebro, ISSN 1403-0586 ; 6
    National Category
    Probability Theory and Statistics
    Research subject
    Statistics
    Identifiers
    urn:nbn:se:oru:diva-12665 (URN)
    Available from: 2010-12-06 Created: 2010-12-06 Last updated: 2017-10-17Bibliographically approved
    3. Statistical properties of a nonparametric measure of discordance in paired ordinal data
    Open this publication in new window or tab >>Statistical properties of a nonparametric measure of discordance in paired ordinal data
    (English)Manuscript (preprint) (Other academic)
    National Category
    Probability Theory and Statistics
    Research subject
    Statistics
    Identifiers
    urn:nbn:se:oru:diva-12666 (URN)
    Available from: 2010-12-06 Created: 2010-12-06 Last updated: 2017-10-17Bibliographically approved
    4. Rank-based methods for analysis of individual variations in paired ordinal data
    Open this publication in new window or tab >>Rank-based methods for analysis of individual variations in paired ordinal data
    (English)Manuscript (preprint) (Other academic)
    National Category
    Probability Theory and Statistics
    Research subject
    Statistics
    Identifiers
    urn:nbn:se:oru:diva-12667 (URN)
    Available from: 2010-12-06 Created: 2010-12-06 Last updated: 2017-10-17Bibliographically approved
    5. Statistical aspects on multiple comparisons of relative rank variance in paired ordinal data
    Open this publication in new window or tab >>Statistical aspects on multiple comparisons of relative rank variance in paired ordinal data
    (English)Manuscript (preprint) (Other academic)
    National Category
    Probability Theory and Statistics
    Research subject
    Statistics
    Identifiers
    urn:nbn:se:oru:diva-12668 (URN)
    Available from: 2010-12-06 Created: 2010-12-06 Last updated: 2017-10-17Bibliographically approved
  • 4.
    Larsson, Kristin
    et al.
    Örebro University, School of Humanities, Education and Social Sciences.
    Nilsson, Linda
    Örebro University, School of Humanities, Education and Social Sciences.
    Samverkan kring barns läsning: -En studie med föräldrar i fokus2008Independent thesis Basic level (professional degree), 10 credits / 15 HE creditsStudent thesis
  • 5.
    Li, Dao
    Örebro University, Örebro University School of Business. School of Technology and Business Studies, Dalarna University, Borlänge, Sweden.
    Common features in vector nonlinear time series models2013Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    This thesis consists of four manuscripts in the area of nonlinear time series econometrics on topics of testing, modeling and forecasting nonlinear common features. The aim of this thesis is to develop new econometric contributions for hypothesis testing and forecasting in thesearea.

    Both stationary and nonstationary time series are concerned. A definition of common features is proposed in an appropriate way to each class. Based on the definition, a vector nonlinear time series model with common features is set up for testing for common features. The proposed models are available for forecasting as well after being well specified.

    The first paper addresses a testing procedure on nonstationary time series. A class of nonlinear cointegration, smooth-transition (ST) cointegration, is examined. The ST cointegration nests the previously developed linear and threshold cointegration. An F-type test for examining the ST cointegration is derived when stationary transition variables are imposed rather than nonstationary variables. Later ones drive the test standard, while the former ones make the test nonstandard. This has important implications for empirical work. It is crucial to distinguish between the cases with stationary and nonstationary transition variables so that the correct test can be used. The second and the fourth papers develop testing approaches for stationary time series. In particular, the vector ST autoregressive (VSTAR) model is extended to allow for common nonlinear features (CNFs). These two papers propose a modeling procedure and derive tests for the presence of CNFs. Including model specification using the testing contributions above, the third paper considers forecasting with vector nonlinear time series models and extends the procedures available for univariate nonlinear models. The VSTAR model with CNFs and the ST cointegration model in the previous papers are exemplified in detail, and thereafter illustrated within two corresponding macroeconomic data sets.

    List of papers
    1. Testing linear cointegration against smooth-transition cointegration
    Open this publication in new window or tab >>Testing linear cointegration against smooth-transition cointegration
    (English)Manuscript (preprint) (Other academic)
    Abstract [en]

    This study studies a smooth-transition (ST) type of cointegration. The proposed ST cointegration allows for regime switching structure in a cointegrated system and incorporates the linear cointegration developed by Engle and Granger (1987) and the threshold cointegration studied by Balke and Fomby (1997). We developed F-type tests to examine linear cointegration against ST cointegration in a class of vector ST cointegrating regression models. The null asymptotic distributions of the tests are derived when stationary transition variables are involved. Finite-sample distributions and the small-sample performances of these tests are studied using Monte Carlo simulations. Our F-type tests have better power when the system contains ST cointegration than when the system is linearly cointegrated. The testing procedure in this study is applied to purchasing power parity (PPP) data as an example, where we observe that there is no linear cointegration, but an ST cointegration exists in the system.

    Keywords
    nonlinear cointegration; threshold cointegration; smooth transition; F test
    National Category
    Mathematics
    Research subject
    Statistics
    Identifiers
    urn:nbn:se:oru:diva-32407 (URN)
    Available from: 2013-11-15 Created: 2013-11-15 Last updated: 2017-10-17Bibliographically approved
    2. Testing common nonlinear features in vector nonlinear autoregressive models
    Open this publication in new window or tab >>Testing common nonlinear features in vector nonlinear autoregressive models
    (English)Manuscript (preprint) (Other academic)
    Abstract [en]

    This paper studies a special class of vector smooth-transition autoregressive (VSTAR) models, which contains common nonlinear features (CNFs). We proposed a triangular representation for these models and developed a procedure for testing CNFs in a VSTAR model. We first test a unit root against a stationary STAR process for each individual time series and subsequently examine whether CNFs exist in the system with the Lagrange Multiplier (LM) test if unit root is rejected in the first step. The LM test has a standard Chi-square asymptotic distribution. The critical values of our unit root tests and finite-sample properties of the F form of our LM test are studied by Monte Carlo simulations. We illustrate how to test and model CNFs using the monthly growth of consumption and income data for the United States (1985:1 to 2011:11). 

    Keywords
    Common features, Lagrange Multiplier test, Vector STAR models
    National Category
    Mathematics
    Research subject
    Mathematics
    Identifiers
    urn:nbn:se:oru:diva-32409 (URN)
    Available from: 2013-11-15 Created: 2013-11-15 Last updated: 2017-10-17Bibliographically approved
    3. Forecasting with vector nonlinear time series models
    Open this publication in new window or tab >>Forecasting with vector nonlinear time series models
    (English)Manuscript (preprint) (Other academic)
    Abstract [en]

    In this study, vector nonlinear time series models are used for forecasting. Point forecasts are numerically obtained via bootstrapping. Our procedure is illustrated by two examples, each of which involves an application of macroeconomic data. Point forecast evaluation concentrates on forecast equality and encompassing. From these two applications, the forecasts from nonlinear models contribute useful information absent in the forecasts from linear models.

    Keywords
    point forecast, forecast evaluation, nonlinearity
    National Category
    Mathematics
    Research subject
    Statistics
    Identifiers
    urn:nbn:se:oru:diva-32410 (URN)
    Available from: 2013-11-15 Created: 2013-11-15 Last updated: 2017-10-17Bibliographically approved
    4. Residual-based inference for common nonlinear features
    Open this publication in new window or tab >>Residual-based inference for common nonlinear features
    (English)Manuscript (preprint) (Other academic)
    Abstract [en]

    This paper investigates common nonlinear features (CNFs) in multivariate nonlinear autoregressive models via testing the residuals. A Wald-type test is proposed, and it is asymptotically Chi-square distributed. Simulation evidence is given to examine the finite-sample properties of the proposed test and, furthermore, to provide a bootstrap version of the test. In addition to the empirical size and power, a specification of the reduced-rank matrix coefficient is studied to measure the departure from the null of CNFs. As the model moves further from containing CNFs, the power of the test increases substantially. Bootstrap critical values are used to improve the empirical size and power, especially when the test has size distortions. To perform a bootstrap version of the test, an algorithm is also provided for the estimation using nonlinear reduced-rank regression (NRRR).

    Keywords
    common features, nonlinearity, residual-based test, bootstrap test, reduced-rank regression
    National Category
    Mathematics
    Research subject
    Statistics
    Identifiers
    urn:nbn:se:oru:diva-32411 (URN)
    Available from: 2013-11-15 Created: 2013-11-15 Last updated: 2017-10-17Bibliographically approved
  • 6.
    Rota, Bernardo João
    Örebro University, Örebro University School of Business.
    Calibration Adjustment for Nonresponse in Sample Surveys2016Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    In this thesis, we discuss calibration estimation in the presence of nonresponse with a focus on the linear calibration estimator and the propensity calibration estimator, along with the use of different levels of auxiliary information, that is, sample and population levels. This is a fourpapers- based thesis, two of which discuss estimation in two steps. The two-step-type estimator here suggested is an improved compromise of both the linear calibration and the propensity calibration estimators mentioned above. Assuming that the functional form of the response model is known, it is estimated in the first step using calibration approach. In the second step the linear calibration estimator is constructed replacing the design weights by products of these with the inverse of the estimated response probabilities in the first step. The first step of estimation uses sample level of auxiliary information and we demonstrate that this results in more efficient estimated response probabilities than using population-level as earlier suggested. The variance expression for the two-step estimator is derived and an estimator of this is suggested. Two other papers address the use of auxiliary variables in estimation. One of which introduces the use of principal components theory in the calibration for nonresponse adjustment and suggests a selection of components using a theory of canonical correlation. Principal components are used as a mean to accounting the problem of estimation in presence of large sets of candidate auxiliary variables. In addition to the use of auxiliary variables, the last paper also discusses the use of explicit models representing the true response behavior. Usually simple models such as logistic, probit, linear or log-linear are used for this purpose. However, given a possible complexity on the structure of the true response probability, it may raise a question whether these simple models are effective. We use an example of telephone-based survey data collection process and demonstrate that the logistic model is generally not appropriate.

    List of papers
    1. Comparisons Of Some Weighting Methods For Nonresponse Adjustment
    Open this publication in new window or tab >>Comparisons Of Some Weighting Methods For Nonresponse Adjustment
    2015 (English)In: Lithuaninan Journal of Statistics, ISSN 1392-642X, Vol. 54, no 1, p. 69-83Article in journal (Refereed) Published
    Abstract [en]

    Sample and population auxiliary information have been demonstrated to be useful and yield approximately equal resultsin large samples. Several functional forms of weights are suggested in the literature. This paper studies the properties of calibrationestimators when the functional form of response probability is assumed to be known. The focus is on the difference between populationand sample level auxiliary information, the latter being demonstrated to be more appropriate for estimating the coefficients in theresponse probability model. Results also suggest a two-step procedure, using sample information for model coefficient estimation inthe first step and calibration estimation of the study variable total in the second step.

    Place, publisher, year, edition, pages
    Vilnius: Lietuvos Statistiku Sajunga/Statistics Lithuania, 2015
    Keywords
    calibration, auxiliary variables, response probability, maximum likelihood
    National Category
    Probability Theory and Statistics
    Research subject
    Statistics
    Identifiers
    urn:nbn:se:oru:diva-52792 (URN)
    Available from: 2016-10-04 Created: 2016-10-04 Last updated: 2019-04-08Bibliographically approved
    2. Variance Estimation in Two-Step Calibration for Nonresponse Adjustment
    Open this publication in new window or tab >>Variance Estimation in Two-Step Calibration for Nonresponse Adjustment
    (English)Manuscript (preprint) (Other academic)
    National Category
    Probability Theory and Statistics
    Research subject
    Statistics
    Identifiers
    urn:nbn:se:oru:diva-52794 (URN)
    Available from: 2016-10-04 Created: 2016-10-04 Last updated: 2017-10-18Bibliographically approved
    3. Calibrating on Principal Components in the Presence of Multiple Auxiliary Variables for Nonresponse Adjustment
    Open this publication in new window or tab >>Calibrating on Principal Components in the Presence of Multiple Auxiliary Variables for Nonresponse Adjustment
    2017 (English)In: South African Statistical Journal, ISSN 0038-271X, E-ISSN 1996-8450, Vol. 51, no 1, p. 103-125Article in journal (Refereed) Published
    Place, publisher, year, edition, pages
    South African Statistical Association, 2017
    National Category
    Probability Theory and Statistics
    Research subject
    Statistics
    Identifiers
    urn:nbn:se:oru:diva-52795 (URN)000424620000006 ()2-s2.0-85034217718 (Scopus ID)
    Available from: 2016-10-04 Created: 2016-10-04 Last updated: 2018-02-28Bibliographically approved
    4. On the Use of Auxiliary Variables and Models in Estimation in Surveys with Nonresponse
    Open this publication in new window or tab >>On the Use of Auxiliary Variables and Models in Estimation in Surveys with Nonresponse
    (English)Manuscript (preprint) (Other academic)
    National Category
    Probability Theory and Statistics
    Research subject
    Statistics
    Identifiers
    urn:nbn:se:oru:diva-52796 (URN)
    Available from: 2016-10-04 Created: 2016-10-04 Last updated: 2017-10-18Bibliographically approved
  • 7.
    Wahlström, Helen
    Örebro University, Department of Business, Economics, Statistics and Informatics.
    Nonparametric tests for comparing two treatments by using ordinal data2004Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    This thesis concerns four nonparametric tests for comparing two treatments in a randomized controlled clinical trial when the main response variable generates discrete or continuous ordinal data and measurements are made at baseline and follow-up.

    In the Wilcoxon-Mann-Whitney test (WMW) the baseline values are not taken into account at all, but in the other three tests both baseline and follow-up values are used. In the stratified WMW test (SWMW) the baseline variable is used as a stratification variable; thus the baseline and follow-up values are used in an asymmetric way. Also in a test which is a modified version of one part of the method for analysing paired ordinal data proposed by Svensson (S) and a test which is based on a statistic proposed by Lanke and Svensson (LS) both variables are used, but in more symmetric fashions.

    The four tests are considered mainly concerning their powers to detect differences between the treatments and regarding the impact of the strength of association between the responses at baseline and follow-up on the efficiencies of the tests. The thesis consists of an introductory part and three papers. In the first paper, large-sample aspects of WMW, SWMW, and S are considered when applied to discrete ordinal data, i.e. ordered categorical data. Known asymptotic distribution results for the test statistics used in WMW and SWMW are given together with derivations of corresponding results for the test statistic used in S. Furthermore, results from a simulation study of actual significance levels of the tests are presented. Pitman efficiencies are derived and some numerical examples are given.

    The second paper concerns small-sample aspects of WMW, SWMW, and S when they are performed as permutation tests and applied to ordered categorical data. A method for estimating the power of such tests in a Monte Carlo study is developed further and implemented on some artificial distributions.

    Finally, the third paper deals with the tests WMW, S, and LS when applied to continuous ordinal data, i.e. data which for example are assessments on a visual analogue scale. As in the second paper, the tests are performed as permutation tests and the method for estimating the power of such tests in a Monte Carlo study is used on logistic-normal distributions.

    List of papers
    1. Nonparametric tests for comparing two treatments by using ordered categorical data: the large-sample case
    Open this publication in new window or tab >>Nonparametric tests for comparing two treatments by using ordered categorical data: the large-sample case
    (English)Manuscript (preprint) (Other academic)
    National Category
    Probability Theory and Statistics
    Research subject
    Statistics
    Identifiers
    urn:nbn:se:oru:diva-15802 (URN)
    Available from: 2011-06-07 Created: 2011-06-07 Last updated: 2017-10-17Bibliographically approved
    2. Nonparametric tests for comparing two treatments by using ordered categorical data: the small-sample case
    Open this publication in new window or tab >>Nonparametric tests for comparing two treatments by using ordered categorical data: the small-sample case
    (English)Manuscript (preprint) (Other academic)
    National Category
    Probability Theory and Statistics
    Research subject
    Statistics
    Identifiers
    urn:nbn:se:oru:diva-15803 (URN)
    Available from: 2011-06-07 Created: 2011-06-07 Last updated: 2017-10-17Bibliographically approved
    3. Nonparametric tests for comparing two treatments by using continuous ordinal data
    Open this publication in new window or tab >>Nonparametric tests for comparing two treatments by using continuous ordinal data
    (English)Manuscript (preprint) (Other academic)
    National Category
    Probability Theory and Statistics
    Research subject
    Statistics
    Identifiers
    urn:nbn:se:oru:diva-15804 (URN)
    Available from: 2011-06-07 Created: 2011-06-07 Last updated: 2017-10-17Bibliographically approved
  • 8.
    Westling, Sara
    Örebro University, Swedish Business School at Örebro University.
    Cost efficency of nonresponse rate reduction efforts: an evaluation approach2008Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Nonresponse, the failure to obtain complete measurement on all sample elements, is an increasing problem in sample surveys. Efforts to reduce the nonresponse rate are expensive and their effect may not be as desired. To allocate the limited resources to where they achieve the most error reduction, it is important to consider the cost-efficiency of efforts. This thesis proposes an approach to evaluate the cost-efficiency of the nonresponse rate reduction efforts for a general survey setup, assuming a direct element sampling design. A theoretical framework, under which both the error and the cost can be evaluated, is introduced. The framework extends the existing notion of response probabilities as fixed quantities by introducing a sequence of response probabilities, taking into account the actions and efforts taken by the survey administration during the data collection period.

    In addition to the introductory part, the thesis consists of four papers. In the first paper, the basic theoretical framework for evaluation is developed and expressions for the nonresponse bias of some common reweighting estimators are derived under this framework. Furthermore, expressions for the bias difference are given, general expressions as well as expressions under different specific scenarios. In the second paper, expressions of the total variance are derived for the same estimators and under the same framework as in the first paper. In the third paper, the basic theoretical framework in the first two papers is extended to incorporate survey costs, and a cost efficiency evaluation approach is introduced for an ideal situation where all required quantities are known. The approach consists of three steps, involving pairwise comparisons of cost-efficiency for time points during the data collection period and evaluation of point estimator bias and variance. Two different measures of the cost efficiency are proposed and compared, theoretically and through a small numerical study. In the fourth paper, the evaluation approach is extended to situations that may occur in practice. In the more favorable situations considered, unknown quantities are estimated through Monte Carlo simulations.Simulations are also used in the cases where unverifiable assumptions must be made about unknown quantities, as this facilitates comparisons of the effect on conclusions under different assumptions.

    List of papers
    1. Nonresponse bias for some common estimators and its change over time in the data collection process
    Open this publication in new window or tab >>Nonresponse bias for some common estimators and its change over time in the data collection process
    2004 (English)Report (Other academic)
    Abstract [en]

    In most surveys, the risk of nonresponse is a factor taken into account at the planning stage. Commonly, resources are set aside for a follow-up procedure which aims at reducing the nonresponse rate. However, we should pay attention to the effect of nonresponse, rather than the nonresponse rate itself.

    When considering nonresponse error, i.e. bias and variance, it is not obvious that the resources spent on nonresponse rate reduction efforts are time and money well spent. In this paper we address this issue, focusing on the effect of follow-ups on nonresponse bias. The nonresponse biases for some common estimators are derived, and the change in bias for these estimators is studied under a setup that allows us to take into account the data collection process, and follow-up efforts in particular.

    Place, publisher, year, edition, pages
    Örebro: Örebro universitet, 2004. p. 37
    Series
    ESI Working Paper Series ; 2004:13
    National Category
    Probability Theory and Statistics
    Research subject
    Statistics
    Identifiers
    urn:nbn:se:oru:diva-3023 (URN)
    Available from: 2008-11-03 Created: 2008-11-03 Last updated: 2017-10-18Bibliographically approved
    2. The variance of some common estimators and its components under nonresponse
    Open this publication in new window or tab >>The variance of some common estimators and its components under nonresponse
    2005 (English)Report (Other academic)
    Abstract [en]

    In most surveys, the risk of nonresponse is a factor taken into account at the planning stage. Commonly, resources are set aside for a follow-up procedure which aims at reducing the nonresponse rate. However, we should pay attention to the effect of nonresponse, rather than the nonresponse rate itself. When considering nonresponse error, i.e. bias and variance, it is not obvious that the resources spent on nonresponse rate reduction efforts are time and money well spent.

    In this paper we address this issue, continuing the work begun in Tångdahl (2004), now focusing on the effect of follow-ups on estimator variance. The components of the variance for some common estimators are derived under a setup that allows us to take into account the data collection process, and follow-up efforts in particular.

    Place, publisher, year, edition, pages
    Örebro: Örebro universitet, 2005. p. 33
    Series
    ESI working paper series, ISSN 1403-0586 ; 2005:9
    National Category
    Probability Theory and Statistics
    Research subject
    Statistics
    Identifiers
    urn:nbn:se:oru:diva-3024 (URN)
    Available from: 2008-11-03 Created: 2008-11-03 Last updated: 2017-10-18Bibliographically approved
    3. On the evaluation of the cost efficiency of nonresponse rate reduction efforts: some general considerations
    Open this publication in new window or tab >>On the evaluation of the cost efficiency of nonresponse rate reduction efforts: some general considerations
    2006 (English)Report (Other academic)
    Abstract [en]

    Virtually every survey today suffers from nonresponse to some extent. To counter this, survey administrators and researchers have a host of methods at their disposal, many of which are both expensive and time consuming. Reduction efforts, aiming at reducing the nonresponse rate, are an important part of the data collection process, but commonly also a substantial part of the available survey budget.

    We propose that the effciency of the reduction efforts be evaluated in relation to the costs. In this paper we point in the direction of an evaluation procedure, using a measure of cost effciency, that can be used in an "ideal" situation, where all relevant quantities are known. It can not be applied directly in practice, but will serve as a point of reference when practically feasible approaches are developed.

    Place, publisher, year, edition, pages
    Örebro: Örebro universitet, 2006. p. 31
    Series
    ESI working paper series, ISSN 1403-0586 ; 2006:5
    National Category
    Probability Theory and Statistics
    Research subject
    Statistics
    Identifiers
    urn:nbn:se:oru:diva-3025 (URN)
    Available from: 2008-11-03 Created: 2008-11-03 Last updated: 2017-10-18Bibliographically approved
    4. A simulation approach to evaluate the cost efficiency of nonresponse follow-ups
    Open this publication in new window or tab >>A simulation approach to evaluate the cost efficiency of nonresponse follow-ups
    (English)Manuscript (preprint) (Other academic)
    National Category
    Probability Theory and Statistics
    Research subject
    Statistics
    Identifiers
    urn:nbn:se:oru:diva-3026 (URN)
    Available from: 2008-11-03 Created: 2008-11-03 Last updated: 2017-10-18Bibliographically approved
  • 9.
    Yang, Yishen
    Örebro University, Örebro University School of Business.
    On Rank-invariant Methods for Ordinal Data2017Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Data from rating scale assessments have rank-invariant properties only, which means that the data represent an ordering, but lack of standardized magnitude, inter-categorical distances, and linearity. Even though the judgments often are coded by natural numbers they are not really metric. The aim of this thesis is to further develop the nonparametric rank-based Svensson methods for paired ordinal data that are based on the rank-invariant properties only.

    The thesis consists of five papers. In Paper I the asymptotic properties of the measure of systematic disagreement in paired ordinal data, the Relative Position (RP), and the difference in RP between groups were studied. Based on the findings of asymptotic normality, two tests for analyses of change within group and between groups were proposed. In Paper II the asymptotic properties of rank-based measures, e.g. the Svensson’s measures of systematic disagreement and of additional individual variability were discussed, and a numerical method for approximation was suggested. In Paper III the asymptotic properties of the measures for paired ordinal data, discussed in Paper II, were verified by simulations. Furthermore, the Spearman rank-order correlation coefficient (rs) and the Svensson’s augmented rank-order agreement coefficient (ra) were compared. By demonstrating how they differ and why they differ, it is emphasized that they measure different things. In Paper IV the proposed test in Paper I for comparing two groups of systematic changes in paired ordinal data was compared with other nonparametric tests for group changes, both regarding different approaches of categorising changes. The simulation reveals that the proposed test works better for small and unbalanced samples. Paper V demonstrates that rank invariant approaches can also be used in analysis of ordinal data from multi-item scales, which is an appealing and appropriate alternative to calculating sum scores.

    List of papers
    1. Non-parametric analyses of change within group and between two groups of paired assessments on rating scales
    Open this publication in new window or tab >>Non-parametric analyses of change within group and between two groups of paired assessments on rating scales
    (English)Manuscript (preprint) (Other academic)
    National Category
    Probability Theory and Statistics
    Identifiers
    urn:nbn:se:oru:diva-54444 (URN)
    Available from: 2017-01-11 Created: 2017-01-11 Last updated: 2017-10-18Bibliographically approved
    2. Asymptotic distribution of rank-based measures for paired ordinal data and their use in interval estimations
    Open this publication in new window or tab >>Asymptotic distribution of rank-based measures for paired ordinal data and their use in interval estimations
    (English)Manuscript (preprint) (Other academic)
    National Category
    Probability Theory and Statistics
    Identifiers
    urn:nbn:se:oru:diva-54446 (URN)
    Available from: 2017-01-11 Created: 2017-01-11 Last updated: 2017-10-18Bibliographically approved
    3. Analysing inter-rater agreement: Spearman’s rank order correlation coefficient vs. Svensson’s augmented rank order agreement correlation
    Open this publication in new window or tab >>Analysing inter-rater agreement: Spearman’s rank order correlation coefficient vs. Svensson’s augmented rank order agreement correlation
    (English)Manuscript (preprint) (Other academic)
    National Category
    Probability Theory and Statistics
    Identifiers
    urn:nbn:se:oru:diva-54448 (URN)
    Available from: 2017-01-11 Created: 2017-01-11 Last updated: 2017-10-18Bibliographically approved
    4. Comparison of methods for comparing two groups of paired ordinal data
    Open this publication in new window or tab >>Comparison of methods for comparing two groups of paired ordinal data
    (English)Manuscript (preprint) (Other academic)
    National Category
    Probability Theory and Statistics
    Identifiers
    urn:nbn:se:oru:diva-54449 (URN)
    Available from: 2017-01-11 Created: 2017-01-11 Last updated: 2017-10-18Bibliographically approved
    5. The use of rank-invariant method in analysis of change in multiitem scales
    Open this publication in new window or tab >>The use of rank-invariant method in analysis of change in multiitem scales
    (English)Manuscript (preprint) (Other academic)
    National Category
    Probability Theory and Statistics
    Identifiers
    urn:nbn:se:oru:diva-54450 (URN)
    Available from: 2017-01-11 Created: 2017-01-11 Last updated: 2017-10-18Bibliographically approved
1 - 9 of 9
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf