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dc.contributor.authorLiang, Kung-Yee
dc.contributor.authorZeger, Scott L.
dc.date.accessioned2008-12-11T20:54:43Z
dc.date.available2008-12-11T20:54:43Z
dc.date.issued1991
dc.identifier.citationZeger, 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.htmen_US
dc.identifier.urihttp://jhir.library.jhu.edu/handle/1774.2/32862
dc.description.abstractIn 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.en_US
dc.language.isoen_USen_US
dc.publisherStatistica Sinicaen_US
dc.subjectLogistic regressionen_US
dc.subjectFeedbacken_US
dc.subjectSeemingly unrelated regressionsen_US
dc.subjectGeneralized estimating equationsen_US
dc.subjectGeneralized linear modelen_US
dc.titleFeedback Models for Discrete and Continuous Time Seriesen_US
dc.typeArticleen_US


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