In pursuit of a suitable alternative to least squares estimation in normal linear models : a thesis ... for the degree of Master of Science in Statistics at Massey University

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dc.contributor.authorFletcher, Robert Hugh
dc.date.accessioned2018-01-23T00:02:17Z
dc.date.available2018-01-23T00:02:17Z
dc.date.issued1979
dc.description.abstractA search for an estimator of β in the Normal Linear Model which has better mean squared error properties than the usual least squares estimator is undertaken. The properties of some classical techniques such as restricted least squares, which includes the selection of a subset of the independent variables, are examined, along with more recent techniques such as ridge regression and Bayesian estimators. Most of these can be shown analytically to improve over least squares only when the true parameter vector β is in some subspace of the parameter space. Empirical Bayes estimators are in general difficult to handle analytically, and so several of these are studied by Monte Carlo methods. A particular modification of one of these empirical Bayes estimators is found to improve over least squares over a large region of the parameter space, and it's use is demonstrated on a small data set. Some suggestions for further improvement of this estimator are given and some techniques for further study of estimators by Monte Carlo methods are recommended.en_US
dc.identifier.urihttp://hdl.handle.net/10179/12689
dc.language.isoenen_US
dc.publisherMassey Universityen_US
dc.rightsThe Authoren_US
dc.subjectLeast squaresen_US
dc.subjectRegression analysisen_US
dc.titleIn pursuit of a suitable alternative to least squares estimation in normal linear models : a thesis ... for the degree of Master of Science in Statistics at Massey Universityen_US
dc.typeThesisen_US
massey.contributor.authorFletcher, Robert Hugh
thesis.degree.disciplineStatisticsen_US
thesis.degree.grantorMassey Universityen_US
thesis.degree.levelMastersen_US
thesis.degree.nameMaster of Science (M. Sc.)en_US
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