A Functional Central Limit Theorem for Strong Mixing Stochastic Processes

Donald Andrews () and David Pollard
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David Pollard: Dept. of Statistics, Yale University

No 951, Cowles Foundation Discussion Papers from Cowles Foundation for Research in Economics, Yale University

Abstract: 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.

Keywords: Strong mixing; functional central limit theorem; empirical process (search for similar items in EconPapers)
Pages: 16 pages
Date: 1990-09
Note: CFP 870.
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Published in International Statistical Review (1994), 62(1): 119-132

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