K
Kung Yee Liang
Researcher at Johns Hopkins University
Publications - 50
Citations - 21878
Kung Yee Liang is an academic researcher from Johns Hopkins University. The author has contributed to research in topics: Nuisance parameter & Estimator. The author has an hindex of 29, co-authored 50 publications receiving 20733 citations. Previous affiliations of Kung Yee Liang include National Yang-Ming University & National Health Research Institutes.
Papers
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Longitudinal data analysis using generalized linear models
Kung Yee Liang,Scott L. Zeger +1 more
TL;DR: In this article, an extension of generalized linear models to the analysis of longitudinal data is proposed, which gives consistent estimates of the regression parameters and of their variance under mild assumptions about the time dependence.
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Asymptotic Properties of Maximum Likelihood Estimators and Likelihood Ratio Tests under Nonstandard Conditions
Steven G. Self,Kung Yee Liang +1 more
TL;DR: In this article, the authors derived the asymptotic distribution of maximum likelihood estimators and likelihood ratio statistics, which is the same as the distribution of the projection of the Gaussian random variable.
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The analysis of binary longitudinal data with time independent covariates
TL;DR: In this paper, the authors considered extensions of logistic regression to the case where the binary outcome variable is observed repeatedly for each subject and proposed two working models that lead to consistent estimates of the regression parameters and of their variances under mild assumptions about the time dependence within each subject's data.
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Conditional logistic regression models for correlated binary data
TL;DR: In this paper, a class of conditional logistic regression models for clustered binary data is considered, including the polychotomous logistic model of Rosner (1984) as a special case.
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Minimum Sample Size Estimation to Detect Gene-Environment Interaction in Case-Control Designs
TL;DR: Results presented here demonstrate that case-control designs can be used to detect gene-environment interaction when there is both a common exposure and a highly polymorphic marker of susceptibility.