Zhigen Zhao

On consistency and sparsity for sliced inverse regression in high dimensions

We provide here a framework to analyze the phase transition phenomenon of slice inverse regression (SIR), a supervised dimension reduction technique introduced by \cite{Li:1991}. Under mild conditions, the asymptotic ratio

An empirical Bayes testing procedure for detecting variants in analysis of next generation sequencing data

\rho= \lim p/n

APPLYING MULTIPLE TESTING PROCEDURES TO DETECT CHANGE IN EAST AFRICAN VEGETATION

is the phase transition parameter and the SIR estimator is consistent if and only if

Translational genomics of acquired laryngotracheal stenosis

\rho= 0

Capturing the severity of type II errors in high-dimensional multiple testing

. When dimension

Empirical Bayes Confidence Intervals for Selected Parameters in High-Dimensional Data

p

Sample Size Determination for a Three-Arm Equivalence Trial of Normally Distributed Responses

is greater than

Determining Candidate Single Nucleotide Polymorphisms in Acquired Laryngotracheal Stenosis: Genetic Basis for ALTS

n

Sparse Sliced Inverse Regression Via Lasso

, we propose a diagonal thresholding screening SIR (DT-SIR) algorithm. This method provides us with an estimate of the eigen-space of the covariance matrix of the conditional expectation

Sample size determination for a three-arm equivalence trial of Poisson and negative binomial responses

var(\mathbf{E}[\boldsymbol{x}|y])