Inference in skew generalized t-link models for clustered binary outcome via a parameter-expanded EM algorithm.

ABSTRACT: Binary Generalized Linear Mixed Model (GLMM) is the most common method used by researchers to analyze clustered binary data in biological and social sciences. The traditional approach to GLMMs causes substantial bias in estimates due to steady shape of logistic and normal distribution assumptions thereby resulting into wrong and misleading decisions. This study brings forward an approach governed by skew generalized t distributions that belong to a class of potentially skewed and heavy tailed distributions. Interestingly, both the traditional logistic and probit mixed models, as well as other available methods can be utilized within the skew generalized t-link model (SGTLM) frame. We have taken advantage of the Expectation-Maximization algorithm accelerated via parameter-expansion for mode

SUBMITTER: Tovissode CF 

PROVIDER: S-EPMC8028747 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature