Graduate Thesis Or Dissertation
 

Post stratified estimation using a known auxiliary variable

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https://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/hd76s281g

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  • Post stratification is considered desirable in sample surveys for two reasons - it reduces the mean squared error when averaged over all possible samples, and it reduces the conditional bias when conditioned on stratum sample sizes. The problem studied in this thesis is post stratified estimation of a finite population mean when there is a known auxiliary variable for each population unit. The primary direction of the thesis follows the lines of Holt and Smith (1979). A method is given for using the auxiliary variable in selection of the stratum boundaries and, using this approach to determine strata, to compare post stratified estimates with the self -weighting estimates from the analytical and empirical points of view. Estimates studied are: the post stratified mean, the post stratified combined ratio, and the post stratified separate ratio. The thesis contains simulation results that explore the distributions of the self -weighting estimates, and the post stratified estimates using conditional and unconditional inferences. The correct coverage properties of the confidence intervals are compared and the design effect, i.e. the ratio of the variance of the self -weighting to the variance of post stratified estimates, is calculated from the samples and its distribution explored by the simulation study for several real and artificial populations. The confidence intervals of post stratified estimates using conditional variances had good coverage properties for each sample configuration used, and hence the correct coverage property over all possible samples provided that the Central Limit Theorem was applied. The comparisons indicated that post stratification is an effective approach when the boundaries are obtained based on proper stratification using an auxiliary variable. Moreover it is more efficient than estimation based on simple random sampling in reducing the mean squared error. Finally, there is strong evidence that the post stratified estimates are robust against poorly distributed samples, whereas empirical investigations suggested that the self -weighting estimates are very poor when the samples are unbalanced.
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