Statistics Colloquium
April 11 (Fri) 11 a.m.
Alavi Commons Room, 6625 Everett Tower

Quantile-adaptive Model-free Variable Screening for High-dimensional Heterogeneous Data

Hyokyoung (Grace) Hong
Department of Statistics and Probability
Michigan State University

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 in the sense of Zhu, Li, Li, Zhu (2011) as it 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. Extensive numerical studies demonstrate the fine performance of the proposed method for various semiparametric models and its effectiveness to extract quantile-specific information from heteroscedastic data.

All statistics students are expected to attend.


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Department of Statistics
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