stratifyR: An R Package for optimal stratification and sample allocation for univariate populations

K. G. Reddy and M. G. M. Khan

Australian & New Zealand Journal of Statistics, 2020, vol. 62, issue 3, 383-405

Abstract: This R package determines optimal stratification of univariate populations under stratified sampling designs using a parametric‐based method. It determines the optimum strata boundaries (OSB), optimum sample sizes (OSS) and multiple other quantities for the study variable, y, using the best‐fit probability density function of a study variable available from survey data. The method requires the parameters and other characteristics of the distribution of the study variable to be known, either from available data or from a hypothetical distribution if the data are not available. In the implementation, the problem of determining the OSB is formulated as a mathematical programming problem and solved by using a dynamic programming technique. If the data of the population (i.e. the study variable) are 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 realisations of proxy values of y from past/recent surveys. Thus, it requires certain distributional assumptions about the study variable. At present, the package handles stratification for the populations where the study variable follows a continuous distribution: namely, Pareto, Triangular, Right‐triangular, Weibull, Gamma, Exponential, Uniform, Normal, Lognormal and Cauchy distributions. In this paper, applications of major functionalities in the package are illustrated with a number of real/simulated as well as some hypothetical populations.

Date: 2020
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