Pötscher, Benedikt M. and Schneider, Ulrike (2011): Distributional results for thresholding estimators in high-dimensional Gaussian regression models.
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We study the distribution of hard-, soft-, and adaptive soft-thresholding estimators within a linear regression model where the number of parameters k can depend on sample size n and may diverge with n. In addition to the case of known error-variance, we define and study versions of the estimators when the error-variance is unknown. We derive the finite-sample distribution of each estimator and study its behavior in the large-sample limit, also investigating the effects of having to estimate the variance when the degrees of freedom n-k does not tend to infinity or tends to infinity very slowly. Our analysis encompasses both the case where the estimators are tuned to perform consistent variable selection and the case where the estimators are tuned to perform conservative variable selection. Furthermore, we discuss consistency, uniform consistency and derive the minimax rate under either type of tuning.
|Item Type:||MPRA Paper|
|Original Title:||Distributional results for thresholding estimators in high-dimensional Gaussian regression models|
|Keywords:||Thresholding, Lasso, adaptive Lasso, penalized maximum likelihood, variable selection, finite-sample distribution, asymptotic distribution, variance estimation, minimax rate, high-dimensional model, oracle property|
|Subjects:||C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C13 - Estimation: General
C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C20 - General
C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C52 - Model Evaluation, Validation, and Selection
|Depositing User:||Benedikt Poetscher|
|Date Deposited:||08 Nov 2011 20:13|
|Last Modified:||16 Mar 2017 21:35|
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Distributional results for thresholding estimators in high-dimensional Gaussian regression models. (deposited 28 Jun 2011 13:40)
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