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Stratification of skewed populations

Gunning, Patricia (2006) Stratification of skewed populations. PhD thesis, Dublin City University.

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Abstract

In this research an algorithm is derived for stratifying skewed populations which is much simpler to implement than any of those currently available. It is based on the suggestion by numerous researchers in the field that it is desirable when stratifying skewed populations to arrange for equal coefficients of variation in each subinterval. Our new algorithm makes the breaks in geometric progression and achieves near-equal stratum coefficients of variation when the populations are skewed. Simulation studies on real skewed populations have shown that the new method compares favourably to those commonly used in terms of precision of the estimator of the mean. We also apply the geometric method to the Lavallée-Hidiroglou (1988) algorithm, an iterative method designed specifically for skewed populations. We show that by taking geometric boundaries as the starting points results in most cases in quicker convergence of the algorithm and achieves smaller sample sizes than the default starting points for the same precision. Finally, geometric stratification is applied to the Pareto distribution, a typical model of skewed data. We show that if any finite range of this distribution is broken into a given number of strata, with boundaries obtained using geometric progression, then the stratum coefficients of variation are equal.

Item Type:Thesis (PhD)
Date of Award:2006
Refereed:No
Supervisor(s):Horgan, Jane
Uncontrolled Keywords:populations; skewed distibutions
Subjects:Mathematics > Statistics
Computer Science > Algorithms
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 3.0 License. View License
ID Code:17380
Deposited On:31 Oct 2012 14:23 by Fran Callaghan. Last Modified 31 Oct 2012 14:23

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