Ping-Shou Zhong

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We propose simultaneous tests for coefficients in high-dimensional linear regression models with factorial designs. The proposed tests are designed for the “large p, small n” situations where the conventional F-test is no longer applicable. We derive the asymptotic distribution of the proposed test statistic under the high-dimensional null hypothesis and(More)
The genetic basis of blood pressure often involves multiple genetic factors and their interactions with environmental factors. Gene-environment interaction is assumed to play an important role in determining individual blood pressure variability. Older people are more prone to high blood pressure than younger ones and the risk may not display a linear trend(More)
In linear regression models with high dimensional data, the classical z-test (or t-test) for testing the significance of each single regression coefficient is no longer applicable. This is mainly because the number of covariates exceeds the sample size. In this paper, we propose a simple and novel alternative by introducing the Correlated Predictors(More)
Single variant analysis in genome-wide association studies (GWAS) has been proven to be successful in identifying thousands of genetic variants associated with hundreds of complex diseases. However, these identified variants only explain a small fraction of inheritable variability in many diseases, suggesting that other resources, such as multilevel genetic(More)
Gene-environment (G×E) interaction plays a pivotal role in understanding the genetic basis of complex disease. When environment factors are measured in a continuous scale, one can assess the genetic sensitivity over different environmental conditions on a disease phenotype. Motivated by the increasing awareness of the power of gene set based association(More)
The order of author affiliations is listed incorrectly. Division of Medical Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, China should appear first. The correct list of affiliations is below. Tao He, Jian Sa, Ping-Shou Zhong, Yuehua Cui, * 1 Division of Medical Statistics, School of Public Health, Shanxi Medical(More)
In this section, we provide details of the EM algorithm for obtaining the maximum likelihood estimates (MLE) of θ where θ = (α1, β1, α2, β2, e,A) T , where A = (akk′)k,k′=1,··· ,M are parameters in the transition matrix. To this end, we introduce the following complete data corresponding the observed data X, Y = {Gil, δil,Xil : l = 1, · · · , L} for i = 1,(More)