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• W2806448901 abstract "We introduce a new version of Stein's method that reduces a large class of normal approximation problems to variance bounding exercises, thus making a connection between central limit theorems and concentration of measure. Unlike Skorokhod embeddings, the object whose variance has to be bounded has an explicit formula that makes it possible to carry out the program more easily. As an application, we derive a general CLT for functions that are obtained as combinations of many local contributions, where the definition of `local' itself depends on the data. Several examples are given, including the solution to a nearest-neighbor CLT problem posed by Peter Bickel." @default.
• W2806448901 created "2018-06-13" @default.
• W2806448901 creator A5025655425 @default.
• W2806448901 date "2006-11-08" @default.
• W2806448901 modified "2023-09-23" @default.
• W2806448901 title "A new method of normal approximation. I. Geometric central limit theorems" @default.
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• W2806448901 hasPublicationYear "2006" @default.
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