Imputation-based empirical likelihood inferences for partially nonlinear quantile regression models with missing responses

Xiaoshuang Zhou (), Peixin Zhao and Yujie Gai
Additional contact information
Xiaoshuang Zhou: Dezhou University
Peixin Zhao: Chongqing Technology and Business University
Yujie Gai: Centural University of Finance and Economics

AStA Advances in Statistical Analysis, 2022, vol. 106, issue 4, No 7, 705-722

Abstract: Abstract In this paper, we consider the confidence interval construction for the partially nonlinear models with missing responses at random under the framework of quantile regression. We propose an imputation-based empirical likelihood method to construct statistical inferences for both the unknown parametric vector in the nonlinear function and the nonparametric function and show that the proposed empirical log-likelihood ratios are both asymptotically chi-squared in theory. Furthermore, the confidence region for the parametric vector and the pointwise confidence interval for the nonparametric function are constructed. Some simulation studies are implemented to assess the performances of the proposed estimation method, and simulation results indicate that the proposed method is workable.

Keywords: Partially nonlinear model; Quantile regression; Empirical likelihood; Missing at random; 62G05; 62G20 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10182-022-00441-z Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:alstar:v:106:y:2022:i:4:d:10.1007_s10182-022-00441-z

Ordering information: This journal article can be ordered from
http://www.springer. ... cs/journal/10182/PS2

DOI: 10.1007/s10182-022-00441-z

Access Statistics for this article

AStA Advances in Statistical Analysis is currently edited by Göran Kauermann and Yarema Okhrin

More articles in AStA Advances in Statistical Analysis from Springer, German Statistical Society
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().