F
Fang Han
Researcher at University of Washington
Publications - 97
Citations - 4334
Fang Han is an academic researcher from University of Washington. The author has contributed to research in topics: Estimator & Covariance matrix. The author has an hindex of 23, co-authored 90 publications receiving 3689 citations. Previous affiliations of Fang Han include University of Minnesota & Johns Hopkins University.
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ECA: High Dimensional Elliptical Component Analysis in non-Gaussian Distributions
TL;DR: Theoretically, both nonasymptotic and asymptotic analyses quantifying the theoretical performances of ECA are provided, and it is shown that ECA’s performance is highly related to the effective rank of the covariance matrix.
Journal ArticleDOI
Test selection with application to detecting disease association with multiple SNPs.
Wei Pan,Fang Han,Xiaotong Shen +2 more
TL;DR: The methodology of estimating the power of a given test with a given dataset and the idea of using the estimated power as the criterion for test selection are proposed and a fast simulation-based method to calculate p values is proposed for the test selection procedure and for any method of combining p values.
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Statistical analysis of latent generalized correlation matrix estimation in transelliptical distribution.
TL;DR: In this paper, the authors studied the theoretical properties of the Kendall's tau sample correlation matrix and its transformed version proposed in Han and Liu (2013b) for estimating the population Kendall tau correlation matrix under both spectral and restricted spectral norms.
Proceedings Article
Principal Component Analysis on non-Gaussian Dependent Data
TL;DR: This paper provides the generalization bounds of convergence for both support recovery and parameter estimation of COCA for the dependent data and provides explicit sufficient conditions on the degree of dependence, under which the parametric rate can be maintained.
Journal ArticleDOI
Statistical analysis of latent generalized correlation matrix estimation in transelliptical distribution
TL;DR: The theoretical properties of the Kendall's tau sample correlation matrix and its transformed version proposed in Han and Liu (2013b) are studied and a "sign subgaussian condition" is presented which is sufficient to guarantee that the rank-based correlation matrix estimator attains the optimal rate of convergence.