New families of estimators and test statistics in log-linear models

JOURNAL OF MULTIVARIATE ANALYSIS

卷:99

Article

Martin, Nirian¹  Pardo, Leandro² 

[1] Univ Complutense Madrid, Dept Stat & OR 3, Sch Stat, E-28040 Madrid, Spain

[2] Univ Complutense Madrid, Dept Stat & OR 1, Fac Math, E-28040 Madrid, Spain

关键词: asymptotic distributions;    nested hypotheses;    Poisson sampling;    multinomial sampling;    product-multinomial sampling;    minimum phi-divergence estimator;    phi-divergence test statistics;   

DOI  :  10.1016/j.jmva.2008.01.002

来源: Elsevier

【 摘 要 】

In this paper we consider categorical data that are distributed according to a multinomial, product-multinomial or Poisson distribution whose expected values follow a log-linear model and we study the inference problem of hypothesis testing in a log-linear model setting. The family of test statistics considered is based on the family of phi-divergence measures. The unknown parameters in the log-linear model under consideration are also estimated using phi-divergence measures: Minimum phi-divergence estimators. A simulation study is included to find test statistics that offer an attractive alternative to the Pearson chi-square and likelihood-ratio test statistics. (c) 2008 Elsevier Inc. All rights reserved.

【 授权许可】

Free