We introduce a quantile-adaptive framework for nonlinear variable screening with high-dimensional heterogeneous data. This framework has two distinctive features: (1) it allows the set of active variables to vary across quantiles, thus making it more flexible to accommodate heterogeneity; (2) it is model-free and avoids the difficult task of specifying the form of a statistical model in a high dimensional space. Our nonlinear independence screening procedure employs spline approximations to model the marginal effects at a quantile level of interest. Under appropriate conditions on the quantile functions without requiring the existence of any moments, the new procedure is shown to enjoy the sure screening property in ultra-high dimensions. Furthermore, the quantile-adaptive framework can naturally handle censored data arising in survival analysis. We prove that the sure screening property remains valid when the response variable is subject to random right censoring. Numerical studies confirm the fine performance of the proposed method for various semiparametric models and its effectiveness to extract quantile-specific information from heteroscedastic data.

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Research has shown that personality traits associated with impulsivity influence alcohol use during emerging adulthood, yet relatively few studies have examined how distinct facets of impulsivity are associated with alcohol use and abuse. We examine the influence of impulsivity traits on four patterns of alcohol use including frequency of alcohol use, alcohol-related problems, binge drinking, and alcohol use disorders (AUDs) in a community sample of young individuals (N = 190). In multivariate regression analyses that controlled for peer and parental alcohol use, psychological distress, and developmental correlates (i.e., college, marriage, employment) in emerging adulthood, we found that urgency and sensation seeking were consistently related to all four constructs of alcohol use. The present study suggests that distinct impulsivity traits may play different roles in escalation of alcohol use and development of AUDs during emerging adulthood.

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The coronavirus pandemic has rapidly evolved into an unprecedented crisis. The susceptible-infectious-removed (SIR) model and its variants have been used for modeling the pandemic. However, time-independent parameters in the classical models may not capture the dynamic transmission and removal processes, governed by virus containment strategies taken at various phases of the epidemic. Moreover, few models account for possible inaccuracies of the reported cases. We propose a Poisson model with time-dependent transmission and removal rates to account for possible random errors in reporting and estimate a time-dependent disease reproduction number, which may reflect the effectiveness of virus control strategies. We apply our method to study the pandemic in several severely impacted countries, and analyze and forecast the evolving spread of the coronavirus. We have developed an interactive web application to facilitate readers’ use of our method.