2019年研究生学术前沿讲座(9)--Weighted estimation of conditional mean function with truncated, censored and dependent data

2019年研究生学术前沿讲座(9

 

主 讲 人:梁汉营  教授  同济大学

主题名称:Weighted estimation of conditional mean function with truncated, censored and dependent data

内容简要:

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 $/alpha$-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.

 

时间地点:2019年4月11日(周四)13:00 地点:经济管理学院335

主办学院:经济管理学院

 

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2019.3.28

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