Independent screening in high-dimensional exponential family predictors’ space |
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Authors: | Kofi Placid Adragni |
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Institution: | 1. Math &2. Stat, University of Maryland Baltimore County, 1000 Hilltop Circle, 410 MP Building, Baltimore, MD 21250, USA |
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Abstract: | We present a methodology for screening predictors that, given the response, follow a one-parameter exponential family distributions. Screening predictors can be an important step in regressions when the number of predictors p is excessively large or larger than n the number of observations. We consider instances where a large number of predictors are suspected irrelevant for having no information about the response. The proposed methodology helps remove these irrelevant predictors while capturing those linearly or nonlinearly related to the response. |
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Keywords: | variable screening inverse regression sufficient dimension reduction high dimensionality |
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