Detection of abrupt changes in statistical models

Detection of abrupt changes in statistical models

This dissertation investigates different types of disorder problems by using sequential procedures for on-line implementation. The problem is considered within the framework of detecting abrupt changes in an observed random process when the disorder can occur at unknown times. The focus of this work...

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Main Author: Aviv, David
Other Authors: Therrien,Charles W.
Language:en_US
Published: Monterey, California. Naval Postgraduate School 2014
Online Access:http://hdl.handle.net/10945/43764
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spelling ndltd-nps.edu-oai-calhoun.nps.edu-10945-437642015-02-11T03:55:42Z Detection of abrupt changes in statistical models Aviv, David Therrien,Charles W. Cristi, Roberto Naval Postgraduate School (U.S.) This dissertation investigates different types of disorder problems by using sequential procedures for on-line implementation. The problem is considered within the framework of detecting abrupt changes in an observed random process when the disorder can occur at unknown times. The focus of this work is on quickest detection methods for cumsum procedures implemented for different parametric and nonparametric nonlinearities and their performance evaluation. Both the non-Bayesian (Maximum-Likelihood) and the Bayesian frameworks are presented but the focus is mainly on non-Bayesian methods for which detailed analysis is provided. The use of Brownian motion approximations is also included and provides an additional viewpoint of analyzing the performance for both the non-Bayesian and Bayesian methods. 2014-11-20T21:35:26Z 2014-11-20T21:35:26Z 1991-06 Thesis http://hdl.handle.net/10945/43764 ocm227772341 en_US This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. As such, it is in the public domain, and under the provisions of Title 17, United States Code, Section 105, it may not be copyrighted. Monterey, California. Naval Postgraduate School
collection NDLTD
language en_US
sources NDLTD
description This dissertation investigates different types of disorder problems by using sequential procedures for on-line implementation. The problem is considered within the framework of detecting abrupt changes in an observed random process when the disorder can occur at unknown times. The focus of this work is on quickest detection methods for cumsum procedures implemented for different parametric and nonparametric nonlinearities and their performance evaluation. Both the non-Bayesian (Maximum-Likelihood) and the Bayesian frameworks are presented but the focus is mainly on non-Bayesian methods for which detailed analysis is provided. The use of Brownian motion approximations is also included and provides an additional viewpoint of analyzing the performance for both the non-Bayesian and Bayesian methods.
author2 Therrien,Charles W.
author_facet Therrien,Charles W.
Aviv, David
author Aviv, David
spellingShingle Aviv, David
Detection of abrupt changes in statistical models
author_sort Aviv, David
title Detection of abrupt changes in statistical models
title_short Detection of abrupt changes in statistical models
title_full Detection of abrupt changes in statistical models
title_fullStr Detection of abrupt changes in statistical models
title_full_unstemmed Detection of abrupt changes in statistical models
title_sort detection of abrupt changes in statistical models
publisher Monterey, California. Naval Postgraduate School
publishDate 2014
url http://hdl.handle.net/10945/43764
work_keys_str_mv AT avivdavid detectionofabruptchangesinstatisticalmodels
_version_ 1716730424591384576

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