Feedback Models for Discrete and Continuous Time Series

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dc.contributor.author Liang, Kung-Yee
dc.contributor.author Zeger, Scott L.
dc.date.accessioned 2008-12-11T20:54:43Z
dc.date.available 2008-12-11T20:54:43Z
dc.date.issued 1991
dc.identifier.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 en
dc.identifier.uri http://jhir.library.jhu.edu/handle/1774.2/32862
dc.description.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. en
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dc.description.provenance Made available in DSpace on 2008-12-11T20:54:43Z (GMT). No. of bitstreams: 1 1991--FeedbackModels.pdf: 1544699 bytes, checksum: d5234451764ac53521d8fe8d06b3ebfa (MD5) Previous issue date: 1991 en
dc.language.iso en_US en
dc.publisher Statistica Sinica en
dc.subject Logistic regression en
dc.subject Feedback en
dc.subject Seemingly unrelated regressions en
dc.subject Generalized estimating equations en
dc.subject Generalized linear model en
dc.title Feedback Models for Discrete and Continuous Time Series en
dc.type Article en

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