Comparison of the Efficiency of the Various Algorithms in Stratified Sampling when the Initial Solutions are Determined with Geometric Method

Er, Şebnem

doi:10.5923/j.statistics.20120201.01

Cited by 6 publications

(11 citation statements)

References 5 publications

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“…There are two main directions works on stratification go in, namely stratification as simple as possible though still efficient, and stratification as efficient as possible. For example, Gunning and Horgan (2006) presented a very simple algorithm, called geometric stratification, for the former (recently, Kozak (2011) showed that stratification based on kmeans clustering is more efficient than geometric stratification, and Er (2012) showed that geometric stratification seldom is efficient and often provides strata that are not feasible), while Kozak (2004) and Keskintürk and Er (2007) presented algorithms for the latter. In this paper, the latter problem is dealt with.…”

Section: Introductionmentioning

confidence: 98%

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Comparison of Random Search Method and Genetic Algorithm for Stratification

Kozak

2013

Communications in Statistics - Simulation and Computation

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This note shows that the genetic algorithm proposed by Keskintürk and Er (Computational Statistics and Data Analysis 2007, 52, 53-67) usually provides similarly effective stratification to that of the random search algorithm proposed by Kozak (Statistics in Transition 2004, 6(5), 797-806), although in some situations it can be noticeably less effective. Despite this, it is suggested that quite likely genetic algorithms have potential to be a means of very efficient stratification, especially in complex stratification problems.

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Section: Introductionmentioning

confidence: 98%

“…The question arises, then, as to which of the two algorithms is more efficient. Recently Er (2012) showed…”

Section: Introductionmentioning

confidence: 99%

Comparison of Random Search Method and Genetic Algorithm for Stratification

Kozak

2013

Communications in Statistics - Simulation and Computation

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“…For setting optimal stratum boundaries, a variety of statistical procedures have been developed. Commonly used methods to determine stratum boundaries are the cumulative root frequency method [19], and the Lavallée-Hidiroglou iterative method [20] with Kozak's [21] algorithm [22]. The strata boundaries constructed using these methods are listed in Table A1.…”

Section: Methodsmentioning

confidence: 99%

“…The proportion of built-up land in the 1 km 2 buffer zones around these villages ranged between 1% and 89%, with an overall average of 23%. iterative method [20] with Kozak's [21] algorithm [22]. The strata boundaries constructed using these methods are listed in Table A1.…”

Section: Proportion Of Built-up Areamentioning

confidence: 99%

Construction and Use of a Simple Index of Urbanisation in the Rural–Urban Interface of Bangalore, India

et al. 2017

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Urbanisation is a global trend rapidly transforming the biophysical and socioeconomic structures of metropolitan areas. To better understand (and perhaps control) these processes, more interdisciplinary research must be dedicated to the rural-urban interface. This also calls for a common reference system describing intermediate stages along a rural-urban gradient. The present paper constructs a simple index of urbanisation for villages in the Greater Bangalore Area, using GIS analysis of satellite images, and combining basic measures of building density and distance. The correlation of the two parameters and discontinuities in the frequency distribution of the combined index indicate highly dynamic stages of transformation, spatially clustered in the rural-urban interface. This analysis is substantiated by a qualitative assessment of village morphologies. The index presented here serves as a starting point in a large, coordinated study of rural-urban transitions. It was used to stratify villages for random sampling in order to perform a representative socioeconomic household survey, along with agricultural experiments and environmental assessments in various subsamples. Later on, it will also provide a matrix against which the results can be aligned and evaluated. In this process, the measures and classification systems themselves can be further refined and elaborated.

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“…Many authors have implemented comparative studies on allocation methods under particular constraints, among others Er (2012), Kozak (2014), Keto & Pahkinen (2017) and Clark et al (2017). This paper can be framed in this area of research, being its main aim the comparison of allocation methods used in a business survey framework.…”

Section: Introductionmentioning

confidence: 99%

Revisiting sample allocation methods: a simulation-based comparison

Chiodini

Manzi

Martelli

et al. 2019

Communications in Statistics - Simulation and Computation

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In stratified sampling the problem of optimally allocating the sample size is of primary importance, especially in business surveys when reliable estimates are required both for the overall population and for the domains of studies. To this purpose, in this paper we compare allocation methods via a simulation engine highlighting the effects on the reliability of the estimates due only to the sample allocation design. Allocation methods considered in this comparison are: the Neyman allocation, the uniform and proportional allocations, the Costa allocation, the Bankier allocation, the Interior Point Non Linear Programming allocation and the Robust Optimal Allocation with Uniform Stratum Threshold, an allocation method recently adopted by the Italian National Statistical Institute. The last two methods outperform the others at the stratum level. At the overall sample level they perform better than the others together with the Neyman allocation method.

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Comparison of the Efficiency of the Various Algorithms in Stratified Sampling when the Initial Solutions are Determined with Geometric Method

Abstract: The main aim of this paper is to examine the efficiency of Genetic Algorithm (GA) of Keskintürk and Er

Cited by 6 publications

References 5 publications

Comparison of Random Search Method and Genetic Algorithm for Stratification

Comparison of Random Search Method and Genetic Algorithm for Stratification

Construction and Use of a Simple Index of Urbanisation in the Rural–Urban Interface of Bangalore, India

Revisiting sample allocation methods: a simulation-based comparison

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