Complete moment convergence for negatively orthant dependent random variables and its applications in statistical models

Xuejun Wang (), Yi Wu, Shuhe Hu and Nengxiang Ling
Additional contact information
Xuejun Wang: Anhui University
Yi Wu: Anhui University
Shuhe Hu: Anhui University
Nengxiang Ling: Hefei University of Technology

Statistical Papers, 2020, vol. 61, issue 3, No 11, 1147-1180

Abstract: Abstract In this paper, a general result on complete moment convergence for arrays of rowwise negatively orthant dependent random variables is obtained. In addition, we present some sufficient conditions to prove the complete moment and complete convergences for the variables. As applications, the complete consistency for the estimators of nonparametric and semiparametric regression models based on negatively orthant dependent errors is established by using the complete convergence that we established. A simulation to study the numerical performance of the consistency for the nearest neighbor weight function estimator in semiparametric regression model is given. Our results generalize and improve some corresponding ones for independent random variables and negatively associated random variables.

Keywords: Negatively orthant dependent random variables; Complete moment convergence; Complete consistency; Semiparametric regression model; Nonparametric regression model; 60F15; 62G05; 62G20 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s00362-018-0983-3 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:stpapr:v:61:y:2020:i:3:d:10.1007_s00362-018-0983-3

Ordering information: This journal article can be ordered from
http://www.springer. ... business/journal/362

DOI: 10.1007/s00362-018-0983-3

Access Statistics for this article

Statistical Papers is currently edited by C. Müller, W. Krämer and W.G. Müller

More articles in Statistical Papers from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().