Strong laws for weighted sums of widely orthant dependent random variables and applications
Abstract: In this study, the strong law of large numbers and the convergence rate for weighted sums of non-identically distributed widely orthant dependent random variables are established. As applications, the strong consistency for weighted estimator in nonparametric regression model and the rate of strong consistency for least-squares estimator in multiple linear regression model are obtained. Some numerical simulations are also provided to verify the validity of the theoretical results.
- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
-
Online-Ressource
- Sprache
-
Englisch
- Erschienen in
-
Strong laws for weighted sums of widely orthant dependent random variables and applications ; volume:22 ; number:1 ; year:2024 ; extent:14
Open mathematics ; 22, Heft 1 (2024) (gesamt 14)
- Urheber
-
Zhu, Yong
Wang, Wei
Chen, Kan
- DOI
-
10.1515/math-2024-0027
- URN
-
urn:nbn:de:101:1-2408311547159.723502394643
- Rechteinformation
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
15.08.2025, 07:38 MESZ
Datenpartner
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Zhu, Yong
- Wang, Wei
- Chen, Kan