Tuning parameter selection in econometrics

Fuente: arXiv
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Autore principale: Chetverikov, Denis
Natura: Preprint
Pubblicazione: 2024
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author Chetverikov, Denis
author_facet Chetverikov, Denis
contents I review some of the main methods for selecting tuning parameters in nonparametric and $\ell_1$-penalized estimation. For the nonparametric estimation, I consider the methods of Mallows, Stein, Lepski, cross-validation, penalization, and aggregation in the context of series estimation. For the $\ell_1$-penalized estimation, I consider the methods based on the theory of self-normalized moderate deviations, bootstrap, Stein's unbiased risk estimation, and cross-validation in the context of Lasso estimation. I explain the intuition behind each of the methods and discuss their comparative advantages. I also give some extensions.
format Preprint
id arxiv_https___arxiv_org_abs_2405_03021
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Tuning parameter selection in econometrics
Chetverikov, Denis
Econometrics
Statistics Theory
62-02
I review some of the main methods for selecting tuning parameters in nonparametric and $\ell_1$-penalized estimation. For the nonparametric estimation, I consider the methods of Mallows, Stein, Lepski, cross-validation, penalization, and aggregation in the context of series estimation. For the $\ell_1$-penalized estimation, I consider the methods based on the theory of self-normalized moderate deviations, bootstrap, Stein's unbiased risk estimation, and cross-validation in the context of Lasso estimation. I explain the intuition behind each of the methods and discuss their comparative advantages. I also give some extensions.
title Tuning parameter selection in econometrics
topic Econometrics
Statistics Theory
62-02
url https://arxiv.org/abs/2405.03021