Graph decompositions and variance balanced block designs of experiments

dc.contributor.authorLiu, Meixin
dc.contributor.supervisorDukes, Peter
dc.contributor.supervisorZhou, Julie
dc.date.accessioned2019-08-20T19:36:56Z
dc.date.available2019-08-20T19:36:56Z
dc.date.copyright2019en_US
dc.date.issued2019-08-20
dc.degree.departmentDepartment of Mathematics and Statistics
dc.degree.levelMaster of Science M.Sc.en_US
dc.description.abstractWe study construction methods for variance balanced design (VBD) with both uncorrelated and correlated errors, where block designs are used to investigate several treatment effects. We begin with a review for the development of VBDs when the errors in the linear effects model are uncorrelated. There are several construction methods of VBDs for equal and unequal block sizes. When the errors are correlated, we introduce graph theory to study construction methods of VBDs. We develop new methods via graph decomposition. In addition, we construct block designs such that the covariance matrix of the least squares estimator of treatment effects is completely symmetric. Various applications are presented for certain specific error covariance matrices.en_US
dc.description.scholarlevelGraduateen_US
dc.identifier.urihttp://hdl.handle.net/1828/11056
dc.languageEnglisheng
dc.language.isoenen_US
dc.rightsAvailable to the World Wide Weben_US
dc.subjectVariance balanced block designen_US
dc.subjectGraph decompositionen_US
dc.subjectExperimental designen_US
dc.titleGraph decompositions and variance balanced block designs of experimentsen_US
dc.typeThesisen_US

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