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  • 1.
    Alam, Md. Moudud
    et al.
    Örebro universitet, Handelshögskolan vid Örebro universitet.
    Carling, Kenneth
    Dalarna University, SE 781 88 Borlange, Sweden.
    Computationally feasible estimation of the covariance structure in generalized linear mixed models 2008Inngår i: Journal of Statistical Computation and Simulation, ISSN 0094-9655, E-ISSN 1563-5163, Vol. 78, nr 12, s. 1229-1239Artikkel i tidsskrift (Fagfellevurdert)
    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.

  • 2.
    Mantalos, Panagiotis
    et al.
    Department of Statistics, University of Lund, Lund, Sweden.
    Karagrigoriou, A.
    Bootstrapping the augmented Dickey-Fuller test for unit root using the MDIC2012Inngår i: Journal of Statistical Computation and Simulation, ISSN 0094-9655, E-ISSN 1563-5163, Vol. 82, nr 3, s. 431-443Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    In this paper, we consider the bootstrap procedure for the augmented Dickey–Fuller (ADF) unit root test by implementing the modified divergence information criterion (MDIC, Mantalos et al. [An improved divergence information criterion for the determination of the order of an AR process, Commun. Statist. Comput. Simul. 39(5) (2010a), pp. 865–879; Forecasting ARMA models: A comparative study of information criteria focusing on MDIC, J. Statist. Comput. Simul. 80(1) (2010b), pp. 61–73]) for the selection of the optimum number of lags in the estimated model. The asymptotic distribution of the resulting bootstrap ADF/MDIC test is established and its finite sample performance is investigated through Monte-Carlo simulations. The proposed bootstrap tests are found to have finite sample sizes that are generally much closer to their nominal values, than those tests that rely on other information criteria, like the Akaike information criterion [H. Akaike, Information theory and an extension of the maximum likelihood principle, in Proceedings of the 2nd International Symposium on Information Theory, B.N. Petrov and F. Csáki, eds., Akademiai Kaido, Budapest, 1973, pp. 267–281]. The simulations reveal that the proposed procedure is quite satisfactory even for models with large negative moving average coefficients.

  • 3.
    Mantalos, Panagiotis
    et al.
    Department of Statistics, University of Lund, Lund, Sweden.
    Mattheou, K.
    Department of Mathematics and Statistics, University of Cyprus, Nicosia, Cyprus.
    Karagrigoriou, A.
    Department of Mathematics and Statistics, University of Cyprus, Nicosia, Cyprus.
    Forecasting ARMA models: a comparative study of information criteria focusing on MDIC2010Inngår i: Journal of Statistical Computation and Simulation, ISSN 0094-9655, E-ISSN 1563-5163, Vol. 80, nr 1, s. 61-73Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    This paper deals with the implementation of model selection criteria to data generated by ARMA processes. The recently introduced modified divergence information criterion is used and compared with traditional selection criteria like the Akaike information criterion (AIC) and the Schwarz information criterion (SIC). The appropriateness of the selected model is tested for one- and five-step ahead predictions with the use of the normalized mean squared forecast errors (NMSFE).

  • 4.
    Mantalos, Panagiotis
    et al.
    Department of Statistics , University of Lund , Sweden.
    Zografos, Konstantinos
    Department of Mathematics , University of Ioannina , Greece.
    Interval estimation for a binomial proportion: a bootstrap approach2008Inngår i: Journal of Statistical Computation and Simulation, ISSN 0094-9655, E-ISSN 1563-5163, Vol. 78, nr 12, s. 1249-1263Artikkel i tidsskrift (Fagfellevurdert)
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