Strong convergence for weighted sums of WOD random variables and its application in the EV regression model

Liwang Ding; Caoqing Jiang

Applications of Mathematics (2024)

  • Issue: 1, page 93-111
  • ISSN: 0862-7940

Abstract

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The strong convergence for weighted sums of widely orthant dependent (WOD) random variables is investigated. As an application, we further investigate the strong consistency of the least squares estimator in EV regression model for WOD random variables. A simulation study is carried out to confirm the theoretical results.

How to cite

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Ding, Liwang, and Jiang, Caoqing. "Strong convergence for weighted sums of WOD random variables and its application in the EV regression model." Applications of Mathematics (2024): 93-111. <http://eudml.org/doc/299201>.

@article{Ding2024,
abstract = {The strong convergence for weighted sums of widely orthant dependent (WOD) random variables is investigated. As an application, we further investigate the strong consistency of the least squares estimator in EV regression model for WOD random variables. A simulation study is carried out to confirm the theoretical results.},
author = {Ding, Liwang, Jiang, Caoqing},
journal = {Applications of Mathematics},
keywords = {errors-in-variables regression model; least squares estimator; widely orthant dependent; strong consistency},
language = {eng},
number = {1},
pages = {93-111},
publisher = {Institute of Mathematics, Academy of Sciences of the Czech Republic},
title = {Strong convergence for weighted sums of WOD random variables and its application in the EV regression model},
url = {http://eudml.org/doc/299201},
year = {2024},
}

TY - JOUR
AU - Ding, Liwang
AU - Jiang, Caoqing
TI - Strong convergence for weighted sums of WOD random variables and its application in the EV regression model
JO - Applications of Mathematics
PY - 2024
PB - Institute of Mathematics, Academy of Sciences of the Czech Republic
IS - 1
SP - 93
EP - 111
AB - The strong convergence for weighted sums of widely orthant dependent (WOD) random variables is investigated. As an application, we further investigate the strong consistency of the least squares estimator in EV regression model for WOD random variables. A simulation study is carried out to confirm the theoretical results.
LA - eng
KW - errors-in-variables regression model; least squares estimator; widely orthant dependent; strong consistency
UR - http://eudml.org/doc/299201
ER -

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