Asymptotic normality of conditional density estimation under truncated, censored and dependent data
Liang, Han-Ying; Zhou, Hongbing; Guo, Qiuli
- 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.
- Type of Publication:
- 62E20; 62N01; Asymptotic normality; conditional density; local linear and Nadaraya Watson type estimators; truncated and censored data
- Communications in Statistics - Theory and Methods
- In press