题 目：A Central Limit Theorem for Bootstrap Sample Sums from Non-I.I.D. Models

报告人：加拿大湖首大学李德立教授

时 间：2017年7月21日 13:30-14:30

地 点：数学楼一楼报告厅

摘要：For bootstrap sample sums resulting from a sequences of random variables { }, a very general central limit theorem is established. The random variables { } do not need to be independent or identically distributed or to be of any particular dependence structure. Furthermore, no conditions, including moment conditions, are imposed in general on the marginal distributions of the { }. As a special case of the main result, a result of Liu (1988) concerning independent but not identically distributed { } is extended to a larger class of parent sequences.

This work has been published in Journal of Statistical Planning and Inference 180(2017), 69-80.