Weighted estimation of conditional mean function with truncated, censored and dependent data. Issue 6 (2nd November 2018)

• Record Type: Journal Article
• Authors: Liang, Han-Ying; Iglesias-Pérez, María del Carmen
• Is Part Of: Statistics. Volume 52:Issue 6(2018)
• Page Start: 1249
• Page End: 1269
• Publication Date: 2018-11-02
• Subjects: Asymptotic normality -- Berry-Esseen type bound -- conditional mean function -- truncated and censored data -- weighted estimator -- α-mixing; 62N01 -- 62E20; Mathematical statistics -- Periodicals; 519.505
• DOI: 10.1080/02331888.2018.1506923
• Languages: English
• ISSNs: 0233-1888

Abstract

ABSTRACT: By applying the empirical likelihood method, we construct a new weighted estimator of the conditional mean function for a left-truncated and right-censored model. Assuming that the observations form a stationary α -mixing sequence, we derive weak convergence with a certain rate and prove asymptotic normality of the weighted estimator. The asymptotic normality shows that the weighted estimator preserves the bias, variance, and, more importantly, automatic good boundary behavior of a local linear estimator of the conditional mean function. Also, a Berry-Esseen type bound for the weighted estimator is established. A simulation study is conducted to study the finite sample behavior of the new estimator and a real data application is provided.