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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Some recent developments for regression analysis of multivariate failure time data
TL;DR: This paper presents a survey of models for multivariate failure time data and focuses on recent extensions of the proportional hazards model for multangular failure timeData formulation, parameter interpretation and estimation procedures are considered.
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Approximate likelihood ratios for general estimating functions
John Hanfelt,Kung Yee Liang +1 more
TL;DR: In this article, the authors present approximate likelihood ratios that can be used along with estimating functions when any of these three problems occurs, including multiple roots for the estimating function, a poorly behaved Wald test, or lack of a goodness-of-fit test.
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Empirically derived latent classes of tobacco dependence syndromes observed in recent-onset tobacco smokers: Epidemiological evidence from a national probability sample survey
TL;DR: This study pursued a line of large-sample epidemiological research on tobacco dependence syndromes that may appear during the first 2 years of tobacco smoking, as clinical features begin to emerge, focusing on smokers who just recently started using tobacco.
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Estimating functions and approximate conditional likelihood
TL;DR: In this article, the approximate conditional likelihood (ACLF) method is applied to the estimation of a scalar parameter 0, in the presence of nuisance parameters, and a sufficient condition for both approaches to be equivalent is given, where the role of parameter orthogonality is emphasized.
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Analysis of case-control/family sampling design
Kung Yee Liang,Ann E. Pulver +1 more
TL;DR: The relative merit of matched versus unmatched designs is discussed; statistical methods that are useful for analyzing family data are presented; and sample size formulas for studies of quantitative and qualitative traits are presented.