N
Naisyin Wang
Researcher at University of Michigan
Publications - 93
Citations - 4490
Naisyin Wang is an academic researcher from University of Michigan. The author has contributed to research in topics: Estimator & Covariate. The author has an hindex of 36, co-authored 93 publications receiving 4207 citations. Previous affiliations of Naisyin Wang include Texas A&M University & National Taiwan University.
Papers
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Journal ArticleDOI
Inference for imputation estimators
James M. Robins,Naisyin Wang +1 more
TL;DR: This paper derived an estimator of the asymptotic variance of both single and multiple imputation estimators, assuming a parametric imputation model but allowing for non-and semiparametric analysis models.
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Marginal nonparametric kernel regression accounting for within‐subject correlation
TL;DR: In this paper, an alternative kernel smoothing method is proposed for longitudinal or clustered data with dependence within clusters, and the smallest variance of the new estimator is achieved when the true correlation is assumed.
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Large-sample theory for parametric multiple imputation procedures
Naisyin Wang,James M. Robins +1 more
TL;DR: In this article, the asymptotic variance structure of the resulting estimators is provided, and the relative efficiencies of different imputation procedures are compared to compare the relative efficiency of different methods.
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DNA Microarray Experiments: Biological and Technological Aspects
TL;DR: Basic research on the technology itself and studies to understand process variation remain in their infancy, and the importance of basic research in DNA array technologies to improve the reliability of future experiments is emphasized.
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Efficient Semiparametric Marginal Estimation for Longitudinal/Clustered Data
TL;DR: In this article, marginal generalized semiparametric partially linear models for clustered data were proposed and evaluated using a longitudinal CD4 cell count dataset, showing that properly taking into account the within-subject correlation among the responses can substantially improve efficiency.