Estimation and variable selection for quantile partially linear single-index models

Yuankun Zhang; Heng Lian; Yan Yu

doi:10.1016/j.jmva.2017.09.006

ScienceGate Book Chapters

JOURNAL ARTICLE

Estimation and variable selection for quantile partially linear single-index models

Yuankun Zhang Heng Lian Yan Yu

Year: 2017 Journal: Journal of Multivariate Analysis Vol: 162 Pages: 215-234 Publisher: Elsevier BV

DOI: 10.1016/j.jmva.2017.09.006

Get Full-Text PDF Get Analytical Report

Keywords:

Mathematics Feature selection Estimator Quantile Lasso (programming language) Linear model Oracle Quantile regression Linear regression Nonparametric statistics Model selection Quantile function Asymptotic distribution Consistency (knowledge bases) Mathematical optimization Applied mathematics Statistics Random variable Computer science Artificial intelligence

Metrics

Cited By

1.58

FWCI (Field Weighted Citation Impact)

Refs

0.82

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Statistical Methods and Inference

Physical Sciences → Mathematics → Statistics and Probability

Monetary Policy and Economic Impact

Social Sciences → Economics, Econometrics and Finance → General Economics, Econometrics and Finance

Spatial and Panel Data Analysis

Social Sciences → Economics, Econometrics and Finance → Economics and Econometrics

Estimation and variable selection for quantile partially linear single-index models

Metrics

Citation History

Topics

Related Documents

Estimation and variable selection for generalised partially linear single-index models

Quantile regression and variable selection for partially linear single-index models with missing censoring indicators

Variable selection and estimation for partially linear single-index models with longitudinal data

Efficient estimation and variable selection for partially linear single-index-coefficient regression models

Estimation and variable selection of quantile partially linear additive models for correlated data