Literature DB >> 10985242

On a likelihood-based goodness-of-fit test of the beta-binomial model.

S T Garren1, R L Smith, W W Piegorsch.   

Abstract

When faced with proportion data that exhibit extra-binomial variation, data analysts often consider the beta-binomial distribution as an alternative model to the more common binomial distribution. A typical example occurs in toxicological experiments with laboratory animals, where binary observations on fetuses within a litter are often correlated with each other. In such instances, it may be of interest to test for the goodness of fit of the beta-binomial model; this effort is complicated, however, when there is large variability among the litter sizes. We investigate a recent goodness-of-fit test proposed by Brooks et al. (1997, Biometrics 53, 1097-1115) but find that it lacks the ability to distinguish between the beta-binomial model and some severely non-beta-binomial models. Other tests and models developed in their article are quite useful and interesting but are not examined herein.

Mesh:

Year:  2000        PMID: 10985242     DOI: 10.1111/j.0006-341x.2000.947_1.x

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


  4 in total

1.  Responses of dopaminergic, serotonergic and noradrenergic networks to acute levo-tetrahydropalmatine administration in naïve rats detected at 9.4 T.

Authors:  Xiping Liu; Zheng Yang; Rupeng Li; Jun Xie; Qian Yin; Alan S Bloom; Shi-Jiang Li
Journal:  Magn Reson Imaging       Date:  2011-11-12       Impact factor: 2.546

2.  The Validation of a Beta-Binomial Model for Overdispersed Binomial Data.

Authors:  Jongphil Kim; Ji-Hyun Lee
Journal:  Commun Stat Simul Comput       Date:  2016-11-11       Impact factor: 1.118

3.  Differential effect of isoflurane, medetomidine, and urethane on BOLD responses to acute levo-tetrahydropalmatine in the rat.

Authors:  Xiping Liu; Rupeng Li; Zheng Yang; Anthony G Hudetz; Shi-Jiang Li
Journal:  Magn Reson Med       Date:  2011-12-28       Impact factor: 4.668

4.  Pooling overdispersed binomial data to estimate event rate.

Authors:  Yinong Young-Xu; K Arnold Chan
Journal:  BMC Med Res Methodol       Date:  2008-08-19       Impact factor: 4.615

  4 in total

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