Variable selection and weighted composite quantile estimation of regression parameters with left-truncated data-Reference-Cited by-同舟云学术

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Variable selection and weighted composite quantile estimation of regression parameters with left-truncated data

Published:2017-11-08 Issue:18 Volume:47 Page:4469-4482
ISSN:0361-0926
Container-title:Communications in Statistics - Theory and Methods
language:en
Short-container-title:Communications in Statistics - Theory and Methods

Author:

Yao Mei¹²,Wang Jiang-Feng³,Lin Lu²,Wang Yu-Xin⁴

Affiliation:

1. School of Mathematics, Hefei University of Technology, Hefei, China

2. Shandong University Qilu Securities Institute for Financial Studies, Shandong University, Jinan, China

3. Department of Statistics, Zhejiang Gongshang University, Hongzhou, China

4. School of Economics, Hefei University of Technology, Hefei, China

Funder

National Social Science Fund of China

Publisher

Informa UK Limited

Subject

Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/03610926.2017.1376089

Reference20 articles.

1. A kernel mode estimate under random left truncation and time series model: asymptotic normality

2. Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties

3. Estimation of the truncation probability in the random truncation model

4. Estimation of regression parameters with left truncated data

5. Variable selection and coefficient estimation via composite quantile regression with randomly censored data

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Single-Index Quantile Regression with Left Truncated Data;Journal of Systems Science and Complexity;2022-10

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