Facultade de Fisioterapia

Weighted estimation of conditional mean function with truncated, censored and dependent data

Liang, Han-Ying; Iglesias Pérez, María del Carmen
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.
Year:
2018
Type of Publication:
Article
Keywords:
Asymptotic normality; conditional mean function; truncated and censored data; weighted estimator; Berry Esseen type bound
Journal:
Statistics
Volume:
52
Number:
6
Pages:
1249-1269
Month:
Online - 9 August
Note:
Q4 103/109 h-index 0,606 (JCR2017)
Comments:
MTM2014-55966-P
DOI:
10.1080/02331888.2018.1506923
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