CFDP 951

A Functional Central Limit Theorem for Strong Mixing Stochastic Processes


Publication Date: August 1990

Pages: 16


This paper shows how the modern machinery for generating abstract empirical central limit theorems can be applied to arrays of dependent variables. It develops a bracketing approximation based on a moment inequality for sums of strong mixing arrays, in an effort to illustrate the sorts of difficulty that need to be overcome when adapting the empirical process theory for independent variables. Some suggestions for further development are offered. The paper is largely self-contained.


Strong mixing, functional central limit theorem, empirical process

See CFP: 870