Asset Details
Do you wish to reserve the book?
KOMLÓS–MAJOR–TUSNÁDY APPROXIMATION UNDER DEPENDENCE
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
KOMLÓS–MAJOR–TUSNÁDY APPROXIMATION UNDER DEPENDENCE
Do you wish to request the book?
KOMLÓS–MAJOR–TUSNÁDY APPROXIMATION UNDER DEPENDENCE
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
KOMLÓS–MAJOR–TUSNÁDY APPROXIMATION UNDER DEPENDENCE
2014
Overview
The celebrated results of Komlós, Major and Tusnády [Z. Wahrsch. Verw. Gebiete32 (1975) 111–131;Z. Wahrsch. Verw. Gebiete34 (1976) 33–58] give optimal Wiener approximation for the partial sums of i.i.d. random variables and provide a powerful tool in probability and statistics. In this paper we extend KMT approximation for a large class of dependent stationary processes, solving a long standing open problem in probability theory. Under the framework of stationary causal processes and functional dependence measures of Wu [Proc. Natl. Acad. Sci. USA102 (2005) 14150–14154], we show that, under natural moment conditions, the partial sum processes can be approximated by Wiener process with an optimal rate. Our dependence conditions are mild and easily verifiable. The results are applied to ergodic sums, as well as to nonlinear time series and Volterra processes, an important class of nonlinear processes.
We currently cannot retrieve any items related to this title. Kindly check back at a later time.