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A selective overview of variable selection in high dimensional feature space. (English) Zbl 1180.62080
Summary: High dimensional statistical problems arise from diverse fields of scientific research and technological development. Variable selection plays a pivotal role in contemporary statistical learning and scientific discoveries. The traditional idea of best subset selection methods, which can be regarded as a specific form of penalized likelihood, is computationally too expensive for many modern statistical applications. Other forms of penalized likelihood methods have been successfully developed over the last decade to cope with high dimensionality. They have been widely applied for simultaneously selecting important variables and estimating their effects in high dimensional statistical inference.
We present a brief account of the recent developments of the theory, methods, and implementations for high dimensional variable selection. What limits of the dimensionality such methods can handle, what the role of penalty functions is, and what statistical properties rapidly drive the advances of the field. The properties of non-concave penalized likelihood and its roles in high dimensional statistical modeling are emphasized. We also review some recent advances in ultra-high dimensional variable selection, with emphasis on independence screening and two-scale methods.

MSC:
62H12 Estimation in multivariate analysis
62H99 Multivariate analysis
65C60 Computational problems in statistics (MSC2010)
62J05 Linear regression; mixed models
62J12 Generalized linear models (logistic models)
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