THE CONSISTENCY FOR THE WEIGHTED ESTIMATOR OF NON-PARAMETRIC REGRESSION MODEL BASED ON WIDELY ORTHANT-DEPENDENT ERRORS

DOI10.1017/S0269964817000262zbMath1431.62131OpenAlexW2728535266MaRDI QIDQ5242844

Yi Wu, Xinran Tao, Hao Xia, Xue-jun Wang

Publication date: 7 November 2019

Published in: Probability in the Engineering and Informational Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1017/s0269964817000262

zbMATH Keywords

convergence rateweighted estimatornonparametric regression modelcomplete consistencywidely orthant-dependent errors

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Nonparametric estimation (62G05) Strong limit theorems (60F15)

Related Items (8)

Weak convergence for weighted sums of negatively associated random variables and its application in nonparametric regression models ⋮ Strong representations of the Kaplan-Meier estimator and hazard estimator with censored widely orthant dependent data ⋮ Uniformly complete consistency of frequency polygon estimation for dependent samples and an application ⋮ On complete and complete moment convergence for weighted sums of widely orthant dependent random variables ⋮ Asymptotics of M‐estimator in multivariate linear regression models for a class of random errors ⋮ The asymptotic properties for the estimators of the survival function and failure rate function based on WOD samples ⋮ Probability inequalities for sums of WUOD random variables and their applications ⋮ Complete consistency and convergence rate of the nearest neighbor estimator of the density function based on WOD samples

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