Time-varying copula models for longitudinal data

Esra Kürüm, John Hughes, Runze Li, Saul Shiffman

Research output: Contribution to journalArticle

3 Scopus citations

Abstract

We propose a copula-based joint modeling framework for mixed longitudinal responses. Our approach permits all model parameters to vary with time, and thus will enable researchers to reveal dynamic response-predictor relationships and response-response associations. We call the new class of models timecop because we model dependence using a time-varying copula. We develop a one-step estimation procedure for the timecop parameter vector, and also describe how to estimate standard errors. We investigate the finite sample performance of our procedure via three simulation studies, one of which shows that our procedure performs well under ignorable missingness. We also illustrate the applicability of our approach by analyzing binary and continuous responses from the Women's Interagency HIV Study and a smoking cessation program.

Original languageEnglish (US)
Pages (from-to)203-221
Number of pages19
JournalStatistics and its Interface
Volume11
Issue number2
DOIs
StatePublished - Jan 1 2018

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Applied Mathematics

Fingerprint Dive into the research topics of 'Time-varying copula models for longitudinal data'. Together they form a unique fingerprint.

  • Cite this