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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An effective algorithm for generation of factorial designs with generalized minimum aberration
TL;DR: This paper provides a formal optimization treatment on optimal designs with generalized minimum aberration for fractional factorial designs and develops new lower bounds and optimality results for resolution-III designs.
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Test of significance for high-dimensional longitudinal data.
Ethan X. Fang,Yang Ning,Runze Li +2 more
TL;DR: A new quadratic decorrelated inference function approach is proposed, which simultaneously removes the impact of nuisance parameters and incorporates the correlation to enhance the efficiency of the estimation procedure.
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Uncovering Multiple Pathways to Substance Use: A Comparison of Methods for Identifying Population Subgroups
TL;DR: Differences among the techniques are described, including required data features, strengths and limitations in terms of the flexibility with which outcomes and predictors can be modeled, and the potential of each technique for helping to inform the selection of targets and timing of substance intervention programs.
Posted Content
Nearly Dimension-Independent Sparse Linear Bandit over Small Action Spaces via Best Subset Selection.
TL;DR: This work considers the stochastic contextual bandit problem under the high dimensional linear model and proposes doubly growing epochs and estimating the parameter using the best subset selection method, which is easy to implement in practice and achieves high probability regret with high probability.
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Flexible semiparametric analysis of longitudinal genetic studies by reduced rank smoothing
TL;DR: It is found that misspecifying the baseline function or the genetic effect function in a parametric analysis may lead to a substantially inflated or highly conservative type I error rate on testing and large mean‐squared error on estimation.