Ultra-high Dimensional Variable Screening via Density Weighted Variance
نویسندگان
چکیده
منابع مشابه
Variable Screening in High-dimensional Feature Space
Variable selection in high-dimensional space characterizes many contemporary problems in scientific discovery and decision making. Fan and Lv [8] introduced the concept of sure screening to reduce the dimensionality. This article first reviews the part of their ideas and results and then extends them to the likelihood based models. The techniques are then applied to disease classifications in c...
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A variable screening procedure via correlation learning was proposed in Fan and Lv (2008) to reduce dimensionality in sparse ultra-high dimensional models. Even when the true model is linear, the marginal regression can be highly nonlinear. To address this issue, we further extend the correlation learning to marginal nonparametric learning. Our nonparametric independence screening is called NIS...
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High dimensionality is a growing feature in many areas of contemporary statistics. Variable selection is fundamental to high-dimensional statistical modeling. For problems of large or huge scale pn, computational cost and estimation accuracy are always two top concerns. In a seminal paper, Candes and Tao (2007) propose a minimum l1 estimator, the Dantzig selector, and show that it mimics the id...
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We study the problem of variable selection in convex nonparametric regression. Under the assumption that the true regression function is convex and sparse, we develop a screening procedure to select a subset of variables that contains the relevant variables. Our approach is a two-stage quadratic programming method that estimates a sum of one-dimensional convex functions, followed by one-dimensi...
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ژورنال
عنوان ژورنال: Journal of Biometrics & Biostatistics
سال: 2018
ISSN: 2155-6180
DOI: 10.4172/2155-6180.1000401