{"id":{"repo_id":"nps","oai_identifier":"oai:calhoun.nps.edu:10945/17163"},"canonical_url":"https://search.dev.ndltd.org/etd/nps/oai:calhoun.nps.edu:10945/17163","repository":{"repo_id":"nps","name":"Naval Postgraduate School","base_url":"https://calhoun.nps.edu/server/oai/request"},"display":{"title":"Parameter estimation for a two-state semi-Markov model of a univariate point process.","abstract":"Using the convenient second-order interval properties of a two-state semi-Markov model for a univariate point process, an automated technique for the estimation of the parameters in the model was researched and discussed. The power spectral density of intervals was estimated by the periodogram and a Kolmogorov-Smirnov test of fit was conducted. The asymtotic exponential distribution and independence of the periodogram points were used to calculate an approximate likelihood function. A system of equations was then formed to find the maximum likelihood estimates of the parameters. Since closed-form solutions for the estimates could not be found, an iterative method to stabilize initial guesses of the parameter values was attempted with only limited success. Results on using Kolmogorov-Smirnov type statistics and the spectrum of intervals to test the fit of stochastic process models to data have also been obtained.","abstract_html":"Using the convenient second-order interval properties of a two-state semi-Markov model for a univariate point process, an automated technique for the estimation of the parameters in the model was researched and discussed. The power spectral density of intervals was estimated by the periodogram and a Kolmogorov-Smirnov test of fit was conducted. The asymtotic exponential distribution and independence of the periodogram points were used to calculate an approximate likelihood function. A system of equations was then formed to find the maximum likelihood estimates of the parameters. Since closed-form solutions for the estimates could not be found, an iterative method to stabilize initial guesses of the parameter values was attempted with only limited success. Results on using Kolmogorov-Smirnov type statistics and the spectrum of intervals to test the fit of stochastic process models to data have also been obtained.","abstract_has_math":false,"creators":["Hornback, James Leroy"],"institution":"Monterey, California. Naval Postgraduate School","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Operations Research and Administrative Sciences","school":null,"contributors":[],"advisors":["Lewis, Peter A.W."],"committee_chairs":[],"committee_members":[],"year":1974,"date_issued":"1974-03","date_published":"1974-03","updated_at":"2026-07-27T20:25:08Z","subjects":[],"languages":["en_US"],"rights":["This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. 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The power spectral density of intervals was estimated by the periodogram and a Kolmogorov-Smirnov test of fit was conducted. The asymtotic exponential distribution and independence of the periodogram points were used to calculate an approximate likelihood function. A system of equations was then formed to find the maximum likelihood estimates of the parameters. Since closed-form solutions for the estimates could not be found, an iterative method to stabilize initial guesses of the parameter values was attempted with only limited success. Results on using Kolmogorov-Smirnov type statistics and the spectrum of intervals to test the fit of stochastic process models to data have also been obtained."]},{"key":"dc:title","label":"Title","values":["Parameter estimation for a two-state semi-Markov model of a univariate point process."]}]}],"canonical_facts":{"dc:contributor.advisor":["Lewis, Peter A.W."],"dc:contributor.department":["Operations Research and Administrative Sciences"],"dc:creator":["Hornback, James Leroy"],"dc:date":["March 1974"],"dc:date.accessioned":["2012-11-14T00:00:14Z"],"dc:date.available":["2012-11-14T00:00:14Z"],"dc:date.issued":["1974-03"],"dc:description.abstract":["Using the convenient second-order interval properties of a two-state semi-Markov model for a univariate point process, an automated technique for the estimation of the parameters in the model was researched and discussed. The power spectral density of intervals was estimated by the periodogram and a Kolmogorov-Smirnov test of fit was conducted. The asymtotic exponential distribution and independence of the periodogram points were used to calculate an approximate likelihood function. A system of equations was then formed to find the maximum likelihood estimates of the parameters. Since closed-form solutions for the estimates could not be found, an iterative method to stabilize initial guesses of the parameter values was attempted with only limited success. Results on using Kolmogorov-Smirnov type statistics and the spectrum of intervals to test the fit of stochastic process models to data have also been obtained."],"dc:identifier.uri":["https://hdl.handle.net/10945/17163"],"dc:language.iso":["en_US"],"dc:publisher":["Monterey, California. Naval Postgraduate School"],"dc:rights":["This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States."],"dc:title":["Parameter estimation for a two-state semi-Markov model of a univariate point process."],"dc:type":["Thesis"]},"updated_at":"2026-07-27T20:25:08Z"}