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Strong approximation of empirical process with independent but non-identically distributed random variables

Strong approximation of empirical process with independent but non-identically distributed random... Let {Y t, t=1, 2, ...} be independent random variables with continuous distribution functionsF t(y). For anyy, denote $$s = \bar F_t (y) = \frac{1}{t}\sum\limits_{i = 1}^t {F_t } (y)$$ . The empirical proocess is defind by $$t^{ - \tfrac{1}{2}} R(s,t)$$ where[Figure not available: see fulltext.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Acta Mathematicae Applicatae Sinica Springer Journals

Strong approximation of empirical process with independent but non-identically distributed random variables

Acta Mathematicae Applicatae Sinica , Volume 3 (2) – Jul 15, 2005

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References (6)

Publisher
Springer Journals
Copyright
Copyright © 1987 by Science Press, Beijing, China and Allerton Press, Inc., New York, U.S.A.
Subject
Mathematics; Applications of Mathematics; Math Applications in Computer Science; Theoretical, Mathematical and Computational Physics
ISSN
0168-9673
eISSN
1618-3932
DOI
10.1007/BF02057589
Publisher site
See Article on Publisher Site

Abstract

Let {Y t, t=1, 2, ...} be independent random variables with continuous distribution functionsF t(y). For anyy, denote $$s = \bar F_t (y) = \frac{1}{t}\sum\limits_{i = 1}^t {F_t } (y)$$ . The empirical proocess is defind by $$t^{ - \tfrac{1}{2}} R(s,t)$$ where[Figure not available: see fulltext.]

Journal

Acta Mathematicae Applicatae SinicaSpringer Journals

Published: Jul 15, 2005

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