Literature DB >> 11318180

Hypothesis testing under mixture models: application to genetic linkage analysis.

K Y Liang1, P J Rathouz.   

Abstract

In this paper we propose a new class of statistics to test a simple hypothesis against a family of alternatives characterized by a mixture model. Unlike the likelihood ratio statistic, whose large sample distribution is still unknown in this situation, these new statistics have a simple asymptotic distribution to which to refer under the null hypothesis. Simulation results suggest that it has adequate power in detecting the alternatives. Its application to genetic linkage analysis in the presence of the genetic heterogeneity that motivated this work is emphasized.

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Year:  1999        PMID: 11318180     DOI: 10.1111/j.0006-341x.1999.00065.x

Source DB:  PubMed          Journal:  Biometrics        ISSN: 0006-341X            Impact factor:   2.571


  5 in total

1.  Testing homogeneity in semiparametric mixture case-control models.

Authors:  Chong-Zhi Di; Kwun Chuen Gary Chan; Cheng Zheng; Kung-Yee Liang
Journal:  Commun Stat Theory Methods       Date:  2017-05-25       Impact factor: 0.893

2.  Likelihood ratio testing for admixture models with application to genetic linkage analysis.

Authors:  Chong-Zhi Di; Kung-Yee Liang
Journal:  Biometrics       Date:  2011-03-08       Impact factor: 2.571

3.  Distribution of model-based multipoint heterogeneity lod scores.

Authors:  Chao Xing; Nathan Morris; Guan Xing
Journal:  Genet Epidemiol       Date:  2010-12       Impact factor: 2.135

4.  An Exponential Tilt Mixture Model for Time-to-Event Data to Evaluate Treatment Effect Heterogeneity in Randomized Clinical Trials.

Authors:  Chi Wang; Zhiqiang Tan; Thomas A Louis
Journal:  Biom Biostat Int J       Date:  2014-09-17

5.  A fast score test for generalized mixture models.

Authors:  Rui Duan; Yang Ning; Shuang Wang; Bruce G Lindsay; Raymond J Carroll; Yong Chen
Journal:  Biometrics       Date:  2019-12-31       Impact factor: 2.571

  5 in total

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