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• W2904358159 abstract "Sampling has evolved into a universally accepted approach for gathering information and data mining as it is widely accepted that a reasonably modest-sized sample can sufficiently characterize a much larger population. In stratified sampling designs, the whole population is divided into homogeneous strata in order to achieve higher precision in the estimation. This paper proposes an efficient method of constructing optimum stratum boundaries (OSB) and determining optimum sample size (OSS) for the survey variable. The survey variable may not be available in practice since the variable of interest is unavailable prior to conducting the survey. Thus, the method is based on the auxiliary variable which is usually readily available from past surveys. To illustrate the application as an example using a real data, the auxiliary variable considered for this problem follows Weibull distribution. The stratification problem is formulated as a Mathematical Programming Problem (MPP) that seeks minimization of the variance of the estimated population parameter under Neyman allocation. The solution procedure employs the dynamic programming technique, which results in substantial gains in the precision of the estimates of the population characteristics." @default.
• W2904358159 created "2018-12-22" @default.
• W2904358159 creator A5010931274 @default.
• W2904358159 creator A5015794994 @default.
• W2904358159 date "2018-12-16" @default.
• W2904358159 modified "2023-10-18" @default.
• W2904358159 title "Optimal stratification in stratified designs using weibull-distributed auxiliary information" @default.
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• W2904358159 doi "https://doi.org/10.1080/03610926.2018.1473609" @default.
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