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stratifyR: Optimal Stratification of Univariate Populations

Reddy, Karuna G. and Khan, Mohammad G.M. (2018) stratifyR: Optimal Stratification of Univariate Populations. [Creative Works]

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    Available under License Creative Commons GNU GPL (Software).

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      [img] HTML (stratifyR Package Vignette) - Accepted Version
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        Abstract

        This R package implements the stratification of univariate populations under stratified sampling designs using the method of Khan et al. (2002, 2008, 2015). It determines the Optimum Strata Boundaries (OSB) and Optimum Sample Sizes (OSS) for the study variable, y, using the best-fit frequency distribution of a survey variable (if data is available) or a hypothetical distribution (if data is not available). The method formulates the problem of determining the OSB as mathematical programming problem which is solved by using a dynamic programming technique. If a dataset of the population is available to the surveyor, the method estimates its best-fit distribution and determines the OSB and OSS under Neyman allocation directly. When the dataset is not available, stratification is made based on the assumption that the values of the study variable, y, are available as hypothetical realizations of proxy values of y from recent surveys. Thus, it requires certain distributional assumptions about the study variable. At present, it handles stratification for the populations where the study variable follows a continuous distribution, namely, Pareto, Triangular, Right-triangular, Weibull, Gamma, Exponential, Uniform, Normal, Log-normal and Cauchy distributions.

        Item Type: Creative Works
        Subjects: Q Science > QA Mathematics > QA76 Computer software
        Divisions: Office of the PVC (R&I)
        Faculty of Science, Technology and Environment (FSTE) > School of Computing, Information and Mathematical Sciences
        Depositing User: Karuna Reddy
        Date Deposited: 30 Aug 2018 12:48
        Last Modified: 30 Aug 2018 12:48
        URI: http://repository.usp.ac.fj/id/eprint/10924
        UNSPECIFIED

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