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An iterative local search approach applied to the optimal stratification problem
Author(s) -
Brito José,
Ochi Luiz,
Montenegro Flávio,
Maculan Nelson
Publication year - 2010
Publication title -
international transactions in operational research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.032
H-Index - 52
eISSN - 1475-3995
pISSN - 0969-6016
DOI - 10.1111/j.1475-3995.2010.00773.x
Subject(s) - mathematical optimization , stratification (seeds) , iterative method , stratified sampling , mathematics , metaheuristic , sampling (signal processing) , variance (accounting) , population , set (abstract data type) , algorithm , computer science , statistics , seed dormancy , botany , germination , accounting , filter (signal processing) , dormancy , business , computer vision , biology , demography , sociology , programming language
Stratified sampling is a technique that consists in separating the elements of a population into nonoverlapping groups, called strata. This paper describes a new algorithm to solve the one‐dimensional case, which reduces the stratification problem to just determining strata boundaries. Assuming that the number L of strata and the total sample size n are predetermined, we obtain the strata boundaries by taking into consideration an objective function associated with the variance. In order to solve this problem, we have implemented an algorithm based on the iterative local search metaheuristic. Computational results obtained from a real data set are presented and discussed.