Please use this identifier to cite or link to this item:

Resampling Methods For Markov Processes With No Mixing Constraints

File Size Format  
2017-12-ms-oshiro.pdf 364.62 kB Adobe PDF View/Open

Item Summary

Title:Resampling Methods For Markov Processes With No Mixing Constraints
Authors:Oshiro, Kevin
Contributors:Electrical Engineering (department)
Date Issued:Dec 2017
Publisher:University of Hawaiʻi at Mānoa
Abstract:Jackknife and bootstrap are resampling procedures that can be used to reduce the bias or estimate
the variance of a statistic. These methods are useful because they perform well and are simple
to implement, but an important assumption for their good performance is that of i.i.d. sampling.
Previous analysis of these techniques for processes with memory generally require constraints on
the memory or the mixing.
In this work we adapt the jackknife and bootstrap procedures to estimate the variance of
conditional probability estimates when we have unbounded memory and make no assumptions on
the mixing of the process. We only require that the process satises a continuity condition, which
says that the incremental value of a bit in the past diminishes with increasing distance. We then
analyze the procedures to provide bounds on the bias of the estimates.
Description:M.S. Thesis. University of Hawaiʻi at Mānoa 2017.
Rights:All UHM dissertations and theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission from the copyright owner.
Appears in Collections: M.S. - Electrical Engineering

Please email if you need this content in ADA-compliant format.

Items in ScholarSpace are protected by copyright, with all rights reserved, unless otherwise indicated.