A copula-based Markov chain model for the analysis of binary longitudinal data

Autor:	Gabriel Escarela, Luis Carlos Pérez-Ruiz, Russell J. Bowater
Rok vydání:	2009
Předmět:	Statistics and Probability Markov chain Gaussian Copula (probability theory) symbols.namesake Autoregressive model Bernoulli distribution Probit model symbols Econometrics Applied mathematics Statistics Probability and Uncertainty Marginal distribution Parametric statistics Mathematics
Zdroj:	Journal of Applied Statistics. 36:647-657
ISSN:	1360-0532 0266-4763
DOI:	10.1080/02664760802499287
Popis:	A fully parametric first-order autoregressive (AR(1)) model is proposed to analyse binary longitudinal data. By using a discretized version of a copula, the modelling approach allows one to construct separate models for the marginal response and for the dependence between adjacent responses. In particular, the transition model that is focused on discretizes the Gaussian copula in such a way that the marginal is a Bernoulli distribution. A probit link is used to take into account concomitant information in the behaviour of the underlying marginal distribution. Fixed and time-varying covariates can be included in the model. The method is simple and is a natural extension of the AR(1) model for Gaussian series. Since the approach put forward is likelihood-based, it allows interpretations and inferences to be made that are not possible with semi-parametric approaches such as those based on generalized estimating equations. Data from a study designed to reduce the exposure of children to the sun are used to il...
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::037b5bb6ec4551758ed985bb0d4c5844 https://doi.org/10.1080/02664760802499287 Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
Nepřihlášeným uživatelům se plný text nezobrazuje	K zobrazení výsledku je třeba se přihlásit.