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Resampling Methods For Markov Processes With No Mixing Constraints

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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.
URI:http://hdl.handle.net/10125/62392
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


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