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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.

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Sensitivity and Specificity of Information Criteria

John J. Dziak, +4 more
- 22 Oct 2018 - 
TL;DR: In some cases the comparison of two models using ICs can be viewed as equivalent to a likelihood ratio test, with the different criteria representing different alpha levels and BIC being a more conservative test than AIC.
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Global solutions to folded concave penalized nonconvex learning.

Hongcheng Liu, +2 more
- 01 Apr 2016 - 
TL;DR: It is shown that a class of nonconvex learning problems are equivalent to general quadratic programs, and this equivalence facilitates us in developing mixed integer linear programming reformulations, which admit finite algorithms that find a provably global optimal solution.
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Combined Diffusion Tensor Imaging and Apparent Transverse Relaxation Rate Differentiate Parkinson Disease and Atypical Parkinsonism.

Guangwei Du, +7 more
- 31 Mar 2017 - 
TL;DR: DTI and the apparent transverse relaxation rate provide different but complementary information for different parkinsonisms and may be a superior marker for the differential diagnosis of parkinsonism.
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Error Variance Estimation in Ultrahigh-Dimensional Additive Models

Zhao Chen, +2 more
- 02 Jan 2018 - 
TL;DR: An accurate estimate for error variance in ultrahigh-dimensional sparse additive model is proposed by effectively integrating sure independence screening and refitted cross-validation techniques and the root n consistency and the asymptotic normality of the resulting estimate are established.
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Evaluation of reproducibility for paired functional data

Runze Li, +1 more
- 01 Mar 2005 - 
TL;DR: A measure to assess measurement agreement for functional data which are frequently encountered in medical research and many other research fields is proposed and formulae to compute the standard error and confidence intervals for the proposed measure are derived.