A Central Limit Theorem in Non‐parametric Regression with Truncated, Censored and Dependent Data

DOI10.1111/sjos.12105zbMath1369.62076OpenAlexW1501154281MaRDI QIDQ5177961

Jacobo de Uña-Álvarez, María del Carmen Iglesias-Pérez, Han-Ying Liang

Publication date: 9 March 2015

Published in: Scandinavian Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/sjos.12105

zbMATH Keywords

asymptotic normalitycentral limit theoremnonparametric regression\(\alpha\)-mixingtruncated and censored dataconditional mean functionNadaraya-Watson and local linear smoothing

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Central limit and other weak theorems (60F05) Estimation in survival analysis and censored data (62N02)

Related Items (4)

Local likelihood of quantile difference under left-truncated, right-censored and dependent assumptions ⋮ Weighted estimation of conditional mean function with truncated, censored and dependent data ⋮ Bernstein polynomial of recursive regression estimation with censored data ⋮ Approximate tolerance intervals for nonparametric regression models

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