Facultade de Fisioterapia

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
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