Literature DB >> 35707561

Analysis of mixed correlated overdispersed binomial and ordinal longitudinal responses: LogLindley-Binomial and ordinal random effects model.

Seyede Sedighe Azimi1, Ehsan Bahrami Samani1, Mojtaba Ganjali1.   

Abstract

We propose a new model called LogLindley-Binomial and ordinal joint model with random effects for analyzing mixed overdispersed binomial and ordinal longitudinal responses. A new distribution called the LogLindley-Binomial is presented, which is appropriate for the analysis of overdispersed binomial variables. A full likelihood-based approach is used to obtain maximum likelihood estimates. A comparison between LogLindley-Binomial and Beta-Binomial distributions are given by a simulation study. Also, to illustrate the utility of the proposed model, some simulation studies are conducted. In simulation studies, the performances of the LogLindley-Binomial distribution and the proposed model are well in some situations. Also, the new model's performance for analyzing a real dataset, extracted from the British Household Panel Survey, is studied. The proposed model performs well in comparison with another model for analyzing real data. Finally, the proposed distribution and the new model are found to be applicable for analyzing overdispersed binomial and mixed data.
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Entities:  

Keywords:  BHPS data; LogLindley-Binomial distribution; latent variable model; maximum likelihood method; overdispersed binomial data

Year:  2021        PMID: 35707561      PMCID: PMC9041728          DOI: 10.1080/02664763.2021.1881455

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


  4 in total

1.  Multivariate linear mixed models for multiple outcomes.

Authors:  M Sammel; X Lin; L Ryan
Journal:  Stat Med       Date:  1999 Sep 15-30       Impact factor: 2.373

2.  Mixed models approaches for joint modeling of different types of responses.

Authors:  Anna Ivanova; Geert Molenberghs; Geert Verbeke
Journal:  J Biopharm Stat       Date:  2015-06-22       Impact factor: 1.051

3.  Longitudinal beta-binomial modeling using GEE for overdispersed binomial data.

Authors:  Hongqian Wu; Ying Zhang; Jeffrey D Long
Journal:  Stat Med       Date:  2016-12-05       Impact factor: 2.373

4.  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

  4 in total

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