Binary geometric process model for the modeling of longitudinal binary data with trend

J.S.K. Chan, Yin Ping Leung

Research output: Journal article publicationJournal articleAcademic researchpeer-review

7 Citations (Scopus)

Abstract

We propose the Binary Geometric Process (BGP) model for longitudinal binary data with trends. The Geometric Process (GP) model contains two components to capture the dynamics on a trend: the mean of an underlying renewal process and the ratio which measures the direction and strength of the trend. The GP model is extended to binary data using a latent GP. The statistical inference for the BGP models is conducted using the least-square, maximum likelihood (ML) and Bayesian methods. The model is demonstrated through simulation studies and real data analyzes. Results reveal that all estimators perform satisfactorily and that the ML estimator performs the best. Moreover the BGP model is better than the ordinary logistic regression model. © 2010 The Author(s).
Original languageEnglish
Pages (from-to)505-536
Number of pages32
JournalComputational Statistics
Volume25
Issue number3
DOIs
Publication statusPublished - 1 Jan 2010
Externally publishedYes

Keywords

  • Geometric process
  • Longitudinal binary data
  • Threshold model
  • Trend data

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Computational Mathematics

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