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

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  • Han-Ying Liang
  • Hong-Bing Zhou
  • Qiu-Li Guo

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.

Suggested Citation

  • Han-Ying Liang & Hong-Bing Zhou & Qiu-Li Guo, 2020. "Asymptotic normality of conditional density estimation under truncated, censored and dependent data," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 49(22), pages 5371-5391, November.
    • Handle: RePEc:taf:lstaxx:v:49:y:2020:i:22:p:5371-5391
    DOI: 10.1080/03610926.2019.1619769
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