R
Runze Li
Researcher at Pennsylvania State University
Publications - 304
Citations - 25154
Runze Li is an academic researcher from Pennsylvania State University. The author has contributed to research in topics: Estimator & Feature selection. The author has an hindex of 53, co-authored 272 publications receiving 21336 citations. Previous affiliations of Runze Li include Academia Sinica & Penn State Cancer Institute.
Papers
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New variable selection methods for zero-inflated count data with applications to the substance abuse field
TL;DR: New variable selection methods for the zero-inflated Poisson regression model are developed based on the special features of two large national databases commonly used in the alcoholism and substance abuse field so that the findings can be easily generalized to the real settings.
Posted Content
Nonparametric Covariance Model
TL;DR: A nonparametric model for conditional covariance matrix is proposed, a kernel estimator is developed accordingly, its asymptotic bias and variance are derived, and its asylptotic normality is established.
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Bayesian group Lasso for nonparametric varying-coefficient models with application to functional genome-wide association studies
TL;DR: The fGWAS model, equipped with Bayesian group lassso, will provide a useful tool for genetic and developmental analysis of complex traits or diseases and is proposed for incorporating functional aspects of phenotypic traits into GWAS.
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Some Q-Q Probability Plots to Test Spherical and Elliptical Symmetry
Runze Li,Kai-Tai Fang,Lixing Zhu +2 more
TL;DR: In this article, some probability plots are proposed to test spherical and elliptical symmetry in terms of some invariant statistics under orthogonal transformations, and the empirical percentiles of these correlation coefficients are calculated by simulation.
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Testing multivariate uniformity and its applications
TL;DR: By Monte Carlo simulation, it is found that the finite-sample distributions of the new statistics are well approximated by the standard normal distribution, N(0,1), or the chi-squared distribution, X 2 (2).