Hypothesis testing in unbalanced two-fold nested random models

Marcin Przystalski

Applicationes Mathematicae (2016)

  • Volume: 43, Issue: 1, page 57-78
  • ISSN: 1233-7234

Abstract

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In many applications of linear random models to multilevel data, it is of interest to test whether the random effects variance components are zero. In this paper we propose approximate tests for testing significance of variance components in the unbalanced two-fold nested random model in the presence of non-normality. In the derivations of the asymptotic distributions of the test statistics, as an intermediate result, the explicit form of the asymptotic covariance matrix of the vector of mean squares in this model is obtained. We also study the influence of some special type of designs on the asymptotic covariance matrix and on the distribution of the proposed test statistics.

How to cite

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Marcin Przystalski. "Hypothesis testing in unbalanced two-fold nested random models." Applicationes Mathematicae 43.1 (2016): 57-78. <http://eudml.org/doc/286407>.

@article{MarcinPrzystalski2016,
abstract = {In many applications of linear random models to multilevel data, it is of interest to test whether the random effects variance components are zero. In this paper we propose approximate tests for testing significance of variance components in the unbalanced two-fold nested random model in the presence of non-normality. In the derivations of the asymptotic distributions of the test statistics, as an intermediate result, the explicit form of the asymptotic covariance matrix of the vector of mean squares in this model is obtained. We also study the influence of some special type of designs on the asymptotic covariance matrix and on the distribution of the proposed test statistics.},
author = {Marcin Przystalski},
journal = {Applicationes Mathematicae},
keywords = {asymptotic covariance matrix; asymptotic normality; hypothesis testing},
language = {eng},
number = {1},
pages = {57-78},
title = {Hypothesis testing in unbalanced two-fold nested random models},
url = {http://eudml.org/doc/286407},
volume = {43},
year = {2016},
}

TY - JOUR
AU - Marcin Przystalski
TI - Hypothesis testing in unbalanced two-fold nested random models
JO - Applicationes Mathematicae
PY - 2016
VL - 43
IS - 1
SP - 57
EP - 78
AB - In many applications of linear random models to multilevel data, it is of interest to test whether the random effects variance components are zero. In this paper we propose approximate tests for testing significance of variance components in the unbalanced two-fold nested random model in the presence of non-normality. In the derivations of the asymptotic distributions of the test statistics, as an intermediate result, the explicit form of the asymptotic covariance matrix of the vector of mean squares in this model is obtained. We also study the influence of some special type of designs on the asymptotic covariance matrix and on the distribution of the proposed test statistics.
LA - eng
KW - asymptotic covariance matrix; asymptotic normality; hypothesis testing
UR - http://eudml.org/doc/286407
ER -

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