K
Kung-Yee Liang
Researcher at Johns Hopkins University
Publications - 26
Citations - 23075
Kung-Yee Liang is an academic researcher from Johns Hopkins University. The author has contributed to research in topics: Regression analysis & Marginal model. The author has an hindex of 20, co-authored 26 publications receiving 22463 citations.
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Analysis of longitudinal data
TL;DR: In this paper, a generalized linear model for longitudinal data and transition models for categorical data are presented. But the model is not suitable for categric data and time dependent covariates are not considered.
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Longitudinal data analysis for discrete and continuous outcomes.
Scott L. Zeger,Kung-Yee Liang +1 more
TL;DR: A class of generalized estimating equations (GEEs) for the regression parameters is proposed, extensions of those used in quasi-likelihood methods which have solutions which are consistent and asymptotically Gaussian even when the time dependence is misspecified as the authors often expect.
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Models for longitudinal data: a generalized estimating equation approach.
TL;DR: This article discusses extensions of generalized linear models for the analysis of longitudinal data in which heterogeneity in regression parameters is explicitly modelled and uses a generalized estimating equation approach to fit both classes of models for discrete and continuous outcomes.
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Analysis of Longitudinal Data.
TL;DR: Van Der Heijden et al. as discussed by the authors used correspondence analysis for the analysis of transitions between more than two time points, where the transition matrix is the product of the margins of the table divided by the total sample size.
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Multivariate Regression Analyses for Categorical Data
TL;DR: In this paper, a class of models for the marginal expectations of each response and for pairwise associations are compared with log-linear models, and the robustness and efficiency of each model is discussed.