Variable selection for high-dimensional generalized varying-coefficient models

By Heng Lian. Statistica sinica, 2012

59 citations recorded by the source corpus.

In this paper, we consider the problem of variable selection for high- dimensional generalized varying-coefficient models and propose a polynomial-spline based procedure that simultaneously eliminates irrelevant predictors and estimates the nonzero coefficients. In a "large p, small n" setting, we demonstrate the conver- gence rates of the estimator under suitable regularity assumptions. In particular, we show the adaptive group lasso estimator can correctly select important vari- ables with probability approaching one and the convergence rates for the nonzero coefficients are the same as the oracle estimator (the estimator when the impor- tant variables are known before carrying out statistical analysis). To automatically choose the regularization parameters, we use the extended Bayesian information cri- terion (eBIC) that effectively controls the number of false positives. Monte Carlo simulations are conducted to examine the finite sample performance of the proposed procedures.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/a7c5529671254b11dcf7c0537561a8aca3262f65

How researchers see this paper

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In Artificial Intelligence

  1. Guannan Wang
  2. Li Wang
  3. Jingru Mu
  4. Lijian Yang
  5. Shujie Ma

In Statistics and Probability

  1. Guannan Wang
  2. Jingru Mu
  3. Li Wang
  4. Lijian Yang
  5. Shujie Ma

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