Xin Li
Zhejiang University
4 Papers
2 Citations
Xin Li is an academic researcher from Zhejiang University. The author has contributed to research in topics: Estimator & Proximal Gradient Methods. The author has an hindex of 2, co-authored 4 publications. Previous affiliations of Xin Li include Northwest University (China).
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Papers
Sparse recovery via nonconvex regularized M-estimators over ℓq-balls
TL;DR: This paper analyzes the recovery properties of nonconvex regularized $M$-estimators, under the assumption that the true parameter is of soft sparsity, and notes that for commonly-used regularizers, a simpler decomposition is applicable thanks to the assumption on the regularizer, which helps to construct the estimator with better recovery performance.
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Sparse estimation via 𝓁 q optimization method in high-dimensional linear regression.
TL;DR: A general $q-restricted eigenvalue condition (REC) is introduced and its sufficient conditions are provided in terms of several widely-used regularity conditions such as sparse eigen value condition, restricted isometry property, and mutual incoherence property to exhibit the stable recovery property of the optimization methods.
Reconstruction of behavior-relevant individual brain activity: an individualized fMRI study.
Dongya Wu,Xin Li,Tianzi Jiang +2 more
TL;DR: It is demonstrated that the individual differences in brain activity can be used to predict behavioral measures of individual subjects with high accuracy using the partial least square regression model and reconstructed individual brain activity shows a potential use in precise and personalized medicine.
Reliable heritability estimation using sparse regularization in ultrahigh dimensional genome-wide association studies.
TL;DR: This paper investigates the influences of the fixed and random effect assumption on heritability estimation, and proposes a two-stage strategy by first performing sparse regularization via cross-validated elastic net, and then applying variance estimation methods to construct reliable heritability estimations.