Optimum stratified sampling using prior information

dc.creatorKoti, Kallappa M
dc.date.available2011-02-18T19:46:35Z
dc.date.issued1988-08
dc.degree.departmentMathematicsen_US
dc.description.abstractThe stratified sample allocation problem using prior information concerning strata variances, is considered. Given k random variables Xi, X2, • • •, Xk on a probability space, a Borel measurable function X of Xi, X2, • • •, X^, called a maximal utility function, is defined. A rigorous derivation of its expected value is presented. The definition and expected value of X are repeatedly used to formulate the objective functions used to solve the stratified sample allocation problem. The resulting allocations are called minimax allocations. Assuming prior information in the form of a distribution function on strata variances, a noninformative design which happens to be an alternative to Aggarwal's (1958) allocation, is proposed. If prior information concerning strata coefficients of variation is available, a minimax sampling strategy based on Searis' (1964) work, is presented. Under a normal superpopulation model, assuming locally uniform prior distributions on strata means and variances, two-phase minimax allocations comparable with that of Draper et al. (1968) are developed. Several numerical examples are given to illustrate and compare minimax allocation procedure with other existing procedures.
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/2346/11921en_US
dc.language.isoeng
dc.publisherTexas Tech Universityen_US
dc.rights.availabilityUnrestricted.
dc.subjectEstimation theoryen_US
dc.subjectStatistical decisionen_US
dc.subjectSampling (Statistics)en_US
dc.titleOptimum stratified sampling using prior information
dc.typeDissertation
thesis.degree.departmentMathematics
thesis.degree.departmentMathematics and Statistics
thesis.degree.disciplineMathematics
thesis.degree.grantorTexas Tech University
thesis.degree.levelDoctoral
thesis.degree.namePh.D.

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