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  • 1.
    Andersson, Michael K.
    et al.
    National Institute of Economic Research, Stockholm, Sweden.
    Karlsson, Sune
    Department of Economic Statistics, Stockholm School of Economics, Stockholm, Sweden.
    Bootstrapping Error Component Models2001In: Computational statistics (Zeitschrift), ISSN 0943-4062, E-ISSN 1613-9658, Vol. 16, no 2, p. 221-231Article in journal (Refereed)
    Abstract [en]

    This paper proposes several resampling algorithms suitable for error component models and evaluates them in the context of bootstrap testing. In short, all the algorithms work well and lead to tests with correct or close to correct size. There is thus little or no reason not to use the bootstrap with error component models.

  • 2.
    Flygare, Ann-Marie
    et al.
    Umeå University, Umeå, Sweden.
    Barrlund, Anders
    Umeå University, Umeå, Sweden.
    Efficient implementation of some contextual classification methods1995In: Computational statistics (Zeitschrift), ISSN 0943-4062, E-ISSN 1613-9658, Vol. 10, no 4, p. 327-338Article in journal (Refereed)
  • 3.
    Gredenhoff, Mikael
    et al.
    Dept. of Economic Statistics, Stockholm School of Economics, Stockholm, Sweden.
    Karlsson, Sune
    Dept. of Economic Statistics, Stockholm School of Economics, Stockholm, Sweden.
    Lag-length selection in VAR-models using equal and unequal lag-length procedures1999In: Computational statistics (Zeitschrift), ISSN 0943-4062, E-ISSN 1613-9658, Vol. 14, no 2, p. 171-187Article in journal (Refereed)
    Abstract [en]

    It is well known that inference in vector autoregressive models depends crucially on the choice of lag-length. Various lag-length selection procedures have been suggested and evaluated in the literature. In these evaluations the possibility that the true model may have unequal lag-length has, however, received little attention. In this paper we investigate how sensitive lag-length estimation procedures, based on assumptions of equal or unequal lag-lengths, are to the true model structure. The procedures used in the paper are based on information criteria and we give results for AIC, HQ and BIG. In the Monte Carlo study we generate data from a variety of VAR models with properties similar to macro-economic time-series. We find that the commonly used procedure based on equal lag-length together with AIC and HQ performs well in most cases. The procedure (due to Hsiao) allowing for unequal lag-lengths produce reasonable results when the true model has unequal lag-length. The Hsiao procedure tend to do better than equal lag-length procedures in models with a more complicated lag structure.

  • 4.
    Jentsch, Carlstein
    et al.
    Department of Economics, University of Mannheim, Mannheim, Germany .
    Kreiss, Jens P.
    Institut für Mathematische Stochastik, Technische Universität Braunschweig, Braunschweig, Germany .
    Mantalos, Panagiotis
    Örebro University, Örebro University School of Business.
    Paparoditis, Efstathios
    Department of Mathematics and Statistics, University of Cyprus, Nicosia, Cyprus .
    Hybrid bootstrap aided unit root testing2012In: Computational statistics (Zeitschrift), ISSN 0943-4062, E-ISSN 1613-9658, Vol. 27, no 4, p. 779-797Article in journal (Refereed)
    Abstract [en]

    In this paper, we propose a hybrid bootstrap procedure for augmented Dickey-Fuller (ADF) tests for the presence of a unit root. This hybrid proposal combines a time domain parametric autoregressive fit to the data and a nonparametric correction applied in the frequency domain to capture features that are possibly not represented by the parametric model. It is known that considerable size and power problems can occur in small samples for unit root testing in the presence of an MA parameter using critical values of the asymptotic Dickey-Fuller distribution. The benefit of the sieve bootstrap in this situation has been investigated by Chang and Park (J Time Ser Anal 24:379–400, 2003). They showed asymptotic validity as well as substantial improvements for small sample sizes, but the actual sizes of their bootstrap tests were still quite far away from the nominal size. The finite sample performances of our procedure are extensively investigated through Monte Carlo simulations and compared to the sieve bootstrap approach. Regarding the size of the tests, our results show that the hybrid bootstrap remarkably outperforms the sieve bootstrap.

  • 5.
    Telaar, Anna
    et al.
    Leibniz Institute for Farm Animal Biology, Dummerstorf, Germany.
    Repsilber, Dirk
    Leibniz Institute for Farm Animal Biology, Dummerstorf, Germany.
    Nürnberg, Gerd
    Leibniz Institute for Farm Animal Biology, Dummerstorf, Germany.
    Biomarker discovery: classification using pooled samples2013In: Computational statistics (Zeitschrift), ISSN 0943-4062, E-ISSN 1613-9658, Vol. 28, no 1, p. 67-106Article in journal (Refereed)
    Abstract [en]

    RNA-sample pooling is sometimes inevitable, but should be avoided in classification tasks like biomarker studies. Our simulation framework investigates a two-class classification study based on gene expression profiles to point out howstrong the outcomes of single sample designs differ to those of pooling designs. The results show how the effects of pooling depend on pool size, discriminating pattern, number of informative features and the statistical learning method used (support vector machines with linear and radial kernel, random forest (RF), linear discriminant analysis, powered partial least squares discriminant analysis (PPLS-DA) and partial least squares discriminant analysis (PLS-DA)). As a measure for the pooling effect, we consider prediction error (PE) and the coincidence of important feature sets for classification based on PLS-DA, PPLS-DAand RF. In general, PPLS-DAand PLS-DAshow constant PE with increasing pool size and low PE for patterns for which the convex hull of one class is not a cover of the other class. The coincidence of important feature sets is larger for PLS-DA and PPLS-DA as it is for RF. RF shows the best results for patterns in which the convex hull of one class is a cover of the other class, but these depend strongly on the pool size. We complete the PE results with experimental data whichwe pool artificially. The PE of PPLS-DAand PLS-DAare again least influenced by pooling and are low. Additionally, we show under which assumption the PLS-DA loading weights, as a measure for importance of features regarding classification, are equal for the different designs.

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