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Deli Li, Department of Mathematics, Lakehead University
A Central Limit Theorem for Bootstrap Sample Sums from Non-I.I.D. Models
For bootstrap sample sums resulting from a sequence of random variables {Xn, n≥1}, a very general central limit theorem is established. The random variables {Xn, n≥1} 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 {Xn, n≥1}. As a special case of the main result, a result of Liu (1988) concerning independent but not identically distributed {Xn, n≥1} is extended to a larger class of parent sequences.
This work is joint with Professor Andrew Rosalsky at University of Florida. The research of Deli Li was partially supported by a grant from the Natural Sciences and Engineering Research Council of Canada.
Thursday, June 13, 2019
4 p.m. - 4:50 p.m.
LH3058 (Lazaridis Hall, Room 3058)