Asymptotic normality of conditional density estimation under truncated, censored and dependent data. Issue 22 (16th November 2020)

• Record Type: Journal Article
• Authors: Liang, Han-Ying; Zhou, Hong-Bing; Guo, Qiu-Li
• Is Part Of: Communications in statistics. Volume 49:Issue 22(2020)
• Page Start: 5371
• Page End: 5391
• Publication Date: 2020-11-16
• Subjects: Asymptotic normality -- local linear and Nadaraya-Watson type estimators -- conditional density -- truncated and censored data -- α-mixing; 62N01 -- 62E20; Mathematical statistics -- Periodicals; Mathematics; Statistics; 519.2
• DOI: 10.1080/03610926.2019.1619769
• Languages: English
• ISSNs: 0361-0926

Abstract

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.