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

Liang, Han-Ying; Zhou, Hongbing; Guo, Qiuli

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. © 2019, © 2019 Taylor & Francis Group, LLC.

Year:

2019

Type of Publication:

Article

Keywords:

62E20; 62N01; Asymptotic normality; conditional density; local linear and Nadaraya Watson type estimators; truncated and censored data

Journal:

Communications in Statistics - Theory and Methods

Volume:

In press

DOI:

10.1080/03610926.2019.1619769