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Asymptotic normality of conditional density estimation under truncated, censored and dependent data.

Authors :
Liang, Han-Ying
Zhou, Hong-Bing
Guo, Qiu-Li
Source :
Communications in Statistics: Theory & Methods. 2020, Vol. 49 Issue 22, p5371-5391. 21p.
Publication Year :
2020

Abstract

In this paper, we focus on the left-truncated and right-censored model, and construct the local linear and Nadaraya-Watson type estimators of the conditional density. Under suitable conditions, we establish the asymptotic normality of the proposed estimators when the observations are assumed to be a stationary α-mixing sequence. Finite sample behavior of the estimators is investigated via simulations too. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
03610926
Volume :
49
Issue :
22
Database :
Academic Search Index
Journal :
Communications in Statistics: Theory & Methods
Publication Type :
Academic Journal
Accession number :
146195841
Full Text :
https://doi.org/10.1080/03610926.2019.1619769