Minimum Profile Hellinger Distance Estimation for Semiparametric Simple Linear Regression Model

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dc.contributor.advisorWu, Jingjing
dc.contributor.advisorLi, Haocheng
dc.contributor.authorLi, Jiang
dc.contributor.committeememberWu, Jingjing
dc.contributor.committeememberLi, Haocheng
dc.contributor.committeememberLu, Xuewen
dc.contributor.committeememberZhang, Qingrun
dc.dateWinter Conferral
dc.date.accessioned2023-02-11T00:31:17Z
dc.date.embargolift2023-02-22
dc.date.issued2021-01-06
dc.description.abstractThe simple linear regression model is essential for analyzing the relation between a response variable and a covariate variable, and the importance of simple linear regression model for statistical analysis of data is well documented. This thesis focuses on the semiparametric simple linear regression model where the distribution of the error term is assumed symmetric but otherwise completely unspecified. Under this model, we constructed a robust estimator of the regression coefficient parameters using the minimum Hellinger distance technique. Minimum Hellinger Distance Estimation (MHDE) was first introduced by Beran (1977) for fully parametric models that has been shown to have good efficiency and robustness properties. In the past decade, the MHDE has been extended to semiparametric models. Furthermore, Wu and Karunamuni (2015) introduced the Minimum Profile Hellinger Distance Estimation (MPHDE) for semeparametric models which retains good efficiency and robustness properties of MHDE in parametric models. In this thesis, I constructed an MPHDE for the semiparametric simple linear regression model. We established in theory the consistency of the proposed MPHDE. Finite-sample performance of the proposed estimator was examined via simulation studies and real data applications. Our numerical results showed that the proposed MPHDE has good efficiency and simultaneously is very robust against outlying observations.
dc.identifier.citationLi, J. (2021). Minimum Profile Hellinger Distance Estimation for Semiparametric Simple Linear Regression Model (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.
dc.identifier.urihttp://hdl.handle.net/1880/115831
dc.identifier.urihttps://dx.doi.org/10.11575/PRISM/40725
dc.language.isoenen
dc.language.isoEnglish
dc.publisher.facultyGraduate Studiesen
dc.publisher.facultyScience
dc.publisher.institutionUniversity of Calgaryen
dc.rightsUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.en
dc.subjectRegression model
dc.subjectMinimum hellinger distance
dc.subject.classificationInformation Science
dc.titleMinimum Profile Hellinger Distance Estimation for Semiparametric Simple Linear Regression Model
dc.typemaster thesis
thesis.degree.disciplineMathematics & Statistics
thesis.degree.grantorUniversity of Calgaryen
thesis.degree.grantorUniversity of Calgary
thesis.degree.nameMaster of Science (MSc)
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