The cut-off point based on underlying distribution and cost function
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.
Keywords: LH algorithm; take-all stratum; take-some stratum; truncated gamma distribution; truncated log-normal distribution
Document Type: Research Article
Affiliations: 1: Department of Applied Statistics, Kyonggi University, Suwon, Republic of Korea 2: Department of Statistics, Hankuk University of Foreign Studies, YongIn, Republic of Korea
Publication date: 25 April 2016
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