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

DOI10.21136/am.2023.0004-23OpenAlexW4389482382MaRDI QIDQ6204983

Caoqing Jiang, Liwang Ding

Publication date: 11 April 2024

Published in: Applications of Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.21136/am.2023.0004-23

zbMATH Keywords

least squares estimatorstrong consistencywidely orthant dependenterrors-in-variables regression model

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Strong limit theorems (60F15)

Cites Work

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• Exponential probability inequalities for WNOD random variables and their applications
• An inequality of widely dependent random variables and its applications
• Uniform asymptotics of the finite-time ruin probability for all times
• Basic renewal theorems for random walks with widely dependent increments
• Panel data from time series of cross-sections
• Asymptotic properties of an estimator in nonlinear functional errors-in-variables models with dependent error terms
• On the strong convergence properties for weighted sums of negatively orthant dependent random variables
• Negative association of random variables, with applications
• Asymptotic normality and strong consistency of LS estimators in the EV regression model with NA errors
• Uniform asymptotics for the finite-time ruin probability of a dependent risk model with a constant interest rate
• Complete \(f\)-moment convergence for Sung's type weighted sums and its application to the EV regression models
• Strong laws for weighted sums of random variables satisfying generalized Rosenthal type inequalities
• Some convergence properties for partial sums of widely orthant dependent random variables and their statistical applications
• Strong and weak consistency of least squares estimators in simple linear EV regression models
• Strong and weak consistency of LS estimators in the EV regression model with negatively superadditive-dependent errors
• Bernstein-type inequality for widely dependent sequence and its application to nonparametric regression models
• On complete convergence for widely orthant-dependent random variables and its applications in nonparametric regression models
• Almost Sure Convergence of Weighted Sums for Negatively Associated Random Variables
• Almost certain convergence in double arrays
• Almost sure convergence theorems of weighted sums of random variables
• The Central Limit Theorem for LS Estimator in Simple Linear EV Regression Models

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