The cut-off point based on underlying distribution and cost function

Sang Eun Lee and Key-Il Shin

Journal of Applied Statistics, 2016, vol. 43, issue 6, 1061-1073

Abstract: Cut-off sampling has been widely used for business survey which has the right-skewed population with a long tail. Several methods are suggested to obtain the optimal cut-off point. The LH algorithm suggested by Lavallee and Hidiroglou [6] is commonly used to get the optimum boundaries by minimizing the total sample size with a given precision. In this paper, we suggest a new cut-off point determination method which minimizes a cost function. And that leads to reducing the size of take-all stratum. Also we investigate an optimal cut-off point using a typical parametric estimation method under the assumptions of underlying distributions. Small Monte-Carlo simulation studies are performed in order to compare the new cut-off point method to the LH algorithm. The Korea Transportation Origin -- Destination data are used for real data analysis.

Date: 2016
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DOI: 10.1080/02664763.2015.1089222

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