Feedback Models for Discrete and Continuous Time Series

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Title: Feedback Models for Discrete and Continuous Time Series
Author: Liang, Kung-Yee; Zeger, Scott L.
Abstract: In public health research, it is common to follow a cohort of subjects over time, observing a vector of health indicators and a set of covariates at each of many visits. An objective of analysis is to characterize the inter-dependencies, in particular, the feedback of one response upon another while accounting for the covariates. With Gaussian responses, multivariate autoregressive models that incorporate feedback are commonly used. This paper discusses analogous Markov models for multivariate discrete and mixed discrete/continuous response variables. One special case is an extension of seemingly unrelated regressions to discrete and continuous outcomes. A generalized estimating equations approach that requires correct specification of only conditional means and variances is discussed. The methods are illustrated by a study of infectious diseases and vitamin A deficiency in Indonesian children.
URI: http://jhir.library.jhu.edu/handle/1774.2/32862
Date: 1991
Citation: Zeger, Scott L. and Kung-Yee Liang. "Feedback Models for Discrete and Continuous Time Series" Statistica Sinica 1 (1991):51-64 http://www3.stat.sinica.edu.tw/statistica/j1n1/j1n14/j1n14.htm
Subject: Logistic regression
Feedback
Seemingly unrelated regressions
Generalized estimating equations
Generalized linear model

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