{"id":{"repo_id":"nps","oai_identifier":"oai:calhoun.nps.edu:10945/6791"},"canonical_url":"https://search.dev.ndltd.org/etd/nps/oai:calhoun.nps.edu:10945/6791","repository":{"repo_id":"nps","name":"Naval Postgraduate School","base_url":"https://calhoun.nps.edu/server/oai/request"},"display":{"title":"Development of a Markov Model for Forecasting Continuation Rates for Enlisted Prior Service and Non-Prior Service Personnel in the Selective Marine Corps Reserve (SMCR)","abstract":"The purpose of this thesis is to develop a Markov model to determine the continuation rates for Prior Service and Non-Prior Service enlisted population in the Selected Marine Corps Reserve (SMCR). Determining the end strength for these populations is necessary for reserve manpower planners to balance the force structure to minimize personnel overage or underages that impact training and labor costs, as well as career progression. The results of model validation indicate that models based on annual aggregate monthly transition rates fails the stationarity assumption required of Markov models. These results suggest that the attrition behaviors are seasonal for both enlisted populations leading to numerous states being non stationary in part due to their correlation with seasonality. We recommend developing and employing models with unique transition rates for each month.","abstract_html":"The purpose of this thesis is to develop a Markov model to determine the continuation rates for Prior Service and Non-Prior Service enlisted population in the Selected Marine Corps Reserve (SMCR). Determining the end strength for these populations is necessary for reserve manpower planners to balance the force structure to minimize personnel overage or underages that impact training and labor costs, as well as career progression. The results of model validation indicate that models based on annual aggregate monthly transition rates fails the stationarity assumption required of Markov models. These results suggest that the attrition behaviors are seasonal for both enlisted populations leading to numerous states being non stationary in part due to their correlation with seasonality. We recommend developing and employing models with unique transition rates for each month.","abstract_has_math":false,"creators":["Erhardt, Bruce J., Jr."],"institution":"Monterey, California. Naval Postgraduate School","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Management","school":null,"contributors":[],"advisors":["Chung, Timothy H.","Darken, Chris"],"committee_chairs":[],"committee_members":[],"year":2012,"date_issued":"2012-03","date_published":"2012-03","updated_at":"2026-07-27T20:25:42Z","subjects":[],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10945/6791","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Chung, Timothy H.","Darken, Chris"]},{"key":"dc:contributor.department","label":"Department","values":["Management"]},{"key":"dc:creator","label":"Author","values":["Erhardt, Bruce J., Jr."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["Mar-12"]},{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2012-05-14T18:55:43Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2012-05-14T18:55:43Z"]},{"key":"dc:date.issued","label":"Date","values":["2012-03"]},{"key":"dc:publisher","label":"Institution","values":["Monterey, California. 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These results suggest that the attrition behaviors are seasonal for both enlisted populations leading to numerous states being non stationary in part due to their correlation with seasonality. We recommend developing and employing models with unique transition rates for each month."]},{"key":"dc:title","label":"Title","values":["Development of a Markov Model for Forecasting Continuation Rates for Enlisted Prior Service and Non-Prior Service Personnel in the Selective Marine Corps Reserve (SMCR)"]}]}],"canonical_facts":{"dc:contributor.advisor":["Chung, Timothy H.","Darken, Chris"],"dc:contributor.department":["Management"],"dc:creator":["Erhardt, Bruce J., Jr."],"dc:date":["Mar-12"],"dc:date.accessioned":["2012-05-14T18:55:43Z"],"dc:date.available":["2012-05-14T18:55:43Z"],"dc:date.issued":["2012-03"],"dc:description.abstract":["The purpose of this thesis is to develop a Markov model to determine the continuation rates for Prior Service and Non-Prior Service enlisted population in the Selected Marine Corps Reserve (SMCR). Determining the end strength for these populations is necessary for reserve manpower planners to balance the force structure to minimize personnel overage or underages that impact training and labor costs, as well as career progression. The results of model validation indicate that models based on annual aggregate monthly transition rates fails the stationarity assumption required of Markov models. These results suggest that the attrition behaviors are seasonal for both enlisted populations leading to numerous states being non stationary in part due to their correlation with seasonality. We recommend developing and employing models with unique transition rates for each month."],"dc:identifier.uri":["https://hdl.handle.net/10945/6791"],"dc:publisher":["Monterey, California. Naval Postgraduate School"],"dc:title":["Development of a Markov Model for Forecasting Continuation Rates for Enlisted Prior Service and Non-Prior Service Personnel in the Selective Marine Corps Reserve (SMCR)"],"dc:type":["Thesis"]},"updated_at":"2026-07-27T20:25:42Z"}