Literature DB >> 35707486

EM-test for homogeneity in a two-sample problem with a mixture structure.

Guanfu Liu1, Yuejiao Fu2, Jianjun Zhang3, Xiaolong Pu4, Boying Wang4.   

Abstract

In many applications such as case-control studies with contaminated controls, or the test of a treatment effect in the presence of nonresponders in biological experiments or clinical trials, a two-sample problem with one of the samples having a mixture structure often arises. Due to the importance and wide applications of scale mixtures and location mixtures, we consider in this paper the case that the component densities differ only in scale parameters and the case that the component densities differ only in location parameters, and further construct an EM-test for the two-sample problem under each case. We show that both the EM-tests possess a chi-squared null limiting distribution. The local power analysis and sample size calculations are also investigated. Finally, the simulation studies and real data analysis demonstrate that the proposed EM-tests have better performance than the existing methods.
© 2019 Informa UK Limited, trading as Taylor & Francis Group.

Entities:  

Keywords:  EM-test; homogeneity test; limiting distribution; scale mixtures; two-sample problem

Year:  2019        PMID: 35707486      PMCID: PMC9041583          DOI: 10.1080/02664763.2019.1652254

Source DB:  PubMed          Journal:  J Appl Stat        ISSN: 0266-4763            Impact factor:   1.416


  5 in total

1.  Hypothesis testing in a mixture case-control model.

Authors:  Jing Qin; Kung-Yee Liang
Journal:  Biometrics       Date:  2011-03       Impact factor: 2.571

2.  Mixture models for continuous data in dose-response studies when some animals are unaffected by treatment.

Authors:  D D Boos; C Brownie
Journal:  Biometrics       Date:  1991-12       Impact factor: 2.571

3.  Detecting a major gene in an F2 population.

Authors:  P Loisel; B Goffinet; H Monod; G Montes De Oca
Journal:  Biometrics       Date:  1994-06       Impact factor: 2.571

4.  Detection of a treatment effect when not all experimental subjects will respond to treatment.

Authors:  P I Good
Journal:  Biometrics       Date:  1979-06       Impact factor: 2.571

5.  Differential variability improves the identification of cancer risk markers in DNA methylation studies profiling precursor cancer lesions.

Authors:  Andrew E Teschendorff; Martin Widschwendter
Journal:  Bioinformatics       Date:  2012-04-06       Impact factor: 6.937

  5 in total

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