A general result on complete convergence for weighted sums of linear processes and its statistical applications

DOI10.1080/02331888.2019.1615912zbMath1418.62073OpenAlexW2945250533MaRDI QIDQ5384674

Shuhe Hu, Ming Hu, Xin Deng, Xue-jun Wang

Publication date: 24 June 2019

Published in: Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02331888.2019.1615912

zbMATH Keywords

complete convergencelinear processnonparametric regression modelsemiparametric regression modelcomplete consistency

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Nonparametric regression and quantile regression (62G08) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Asymptotic properties of nonparametric inference (62G20)

Related Items

Complete moment convergence of moving-average processes under END assumptions, A note on the asymptotic properties of the estimators in a semiparametric regression model, Complete consistency for the weighted least squares estimators in semiparametric regression models, Weak consistency for the estimators in a semiparametric regression model based on negatively associated random errors, Complete moment convergence for m-ANA random variables and statistical applications, Convergence in mean and central limit theorems for weighted sums of martingale difference random vectors with infinite rth moments

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