Multiple testing becomes an increasingly important topic in high-dimensional statistical analysis.However,most commonly used false discovery rate estimation and control methods do not take covariates
Many panel studies collect refreshment samples—new,randomly sampled respondents who complete the questionnaire at the same time as a subsequent wave of the panel.
Given p-dimensional Gaussian vectors Xi iid~N(0,Σ),1≤ i ≤ n,where p ≥ n,we are interested in testing a null hypothesis where Σ = Ip against an alternative hypothesis where all eigenvalues of are 1,e
Knowledge of driver genes whose mutations lead to tumor-genesis is important for understanding the mechanisms of cancer and for identifying promising drug targets.
Identifying dominating genes for drug targets must both consider biologically mean-ingful outcomes and employ an ensemble of e ective data analytics for the identi cation.
For complete ultrahigh-dimensional data,sure independent screening methods can effectively reduce the dimensionality while ensuring that all the active variables can be retained with high probability.
The talk will cover two extensions of the sufficient dimensional reduction method.In one project,we develop a semiparametric functional single index model to study the relation between a univariate re
The main challenge in the context of cure rate analysis is that one never knows whether censored subjects are cured or uncured,or whether they are susceptible or insusceptible to the event of interest