The time series regression model was widely studied in the literature by several authors. However, statistical analysis of replicated time series regression models has received little attention. In this thesis, we study the application of quasi-least squares, a relatively new method, to estimate the parameters in replicated time series models with general ARMA( p, q) correlation structure. We also study several established methods for estimating the parameters in those models, including the maximum likelihood, method of moments, and the GEE method. Asymptotic comparisons of the methods are made bV fixing the number of repeated measurements in each series, and letting the number of replications n go to infinity. Our theoretical as well as some simulation results show that the quasi-least squares estimates are undoubtedly better than the moment estimates, and are good competitors and more robust than the maximum likelihood estimates. Examples are presented to illustrate the application of the quasi-least squares method to analyze real life data situations.
","abstract_html":"<p>The time series regression model was widely studied in the literature by several authors. However, statistical analysis of replicated time series regression models has received little attention. In this thesis, we study the application of quasi-least squares, a relatively new method, to estimate the parameters in replicated time series models with general ARMA(<em> p, q</em>) correlation structure. We also study several established methods for estimating the parameters in those models, including the maximum likelihood, method of moments, and the GEE method. Asymptotic comparisons of the methods are made bV fixing the number of repeated measurements in each series, and letting the number of replications <em>n</em> go to infinity. Our theoretical as well as some simulation results show that the quasi-least squares estimates are undoubtedly better than the moment estimates, and are good competitors and more robust than the maximum likelihood estimates. Examples are presented to illustrate the application of the quasi-least squares method to analyze real life data situations.</p>","abstract_has_math":false,"creators":["Shi, Genming"],"institution":null,"degree_name":"Doctor of Philosophy (PhD)","degree_level":"Dissertation","degree_discipline":"Mathematics & Statistics","degree_department":null,"school":null,"contributors":["Narasinga R. Chaganty","Dayanand N. Naik","Ram Dahiya","Larry Filer"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2003,"date_issued":"2003-07-01T07:00:00Z","date_published":"2003-07-01T07:00:00Z","updated_at":"2026-07-24T03:35:00Z","subjects":["Maximum likelihood","Method of moments","Quasi-least squares","Time-series regression","Biostatistics","Longitudinal Data Analysis and Time Series","Statistical Models"],"languages":[],"rights":["In Copyright. URI: http://rightsstatements.org/vocab/InC/1.0/ This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
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"]},{"key":"dc:title","label":"Title","values":["Estimation of Parameters in Replicated Time Series Regression Models"]}]}],"canonical_facts":{"dc:contributor":["Narasinga R. Chaganty","Dayanand N. Naik","Ram Dahiya","Larry Filer"],"dc:creator":["Shi, Genming"],"dc:date.available":["2019-06-14T07:00:00Z"],"dc:description.abstract":["The time series regression model was widely studied in the literature by several authors. However, statistical analysis of replicated time series regression models has received little attention. In this thesis, we study the application of quasi-least squares, a relatively new method, to estimate the parameters in replicated time series models with general ARMA( p, q) correlation structure. We also study several established methods for estimating the parameters in those models, including the maximum likelihood, method of moments, and the GEE method. Asymptotic comparisons of the methods are made bV fixing the number of repeated measurements in each series, and letting the number of replications n go to infinity. Our theoretical as well as some simulation results show that the quasi-least squares estimates are undoubtedly better than the moment estimates, and are good competitors and more robust than the maximum likelihood estimates. Examples are presented to illustrate the application of the quasi-least squares method to analyze real life data situations.
"],"dc:identifier":["9780496549405","https://digitalcommons.odu.edu/mathstat_etds/72"],"dc:rights":["In Copyright. URI: http://rightsstatements.org/vocab/InC/1.0/ This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
"],"dc:subject":["Maximum likelihood","Method of moments","Quasi-least squares","Time-series regression","Biostatistics","Longitudinal Data Analysis and Time Series","Statistical Models"],"dc:title":["Estimation of Parameters in Replicated Time Series Regression Models"],"thesis:degree_discipline":["Mathematics & Statistics"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Doctor of Philosophy (PhD)"]},"updated_at":"2026-07-24T03:35:00Z"}