and other methods for sample allocation in multivariate stratified surveys

Melaku, Alemayehu; Sadasivan, G.

doi:10.1016/0167-9473(87)90064-8

Cited by 7 publications

(3 citation statements)

References 12 publications

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“…There are other applications of L 1 norm in U-statistics by Chun (1987), Bayesian approach by Militky and Cap (1987), isotonic regression by Menendez and Salvador (1987), sample allocation by Melaku and Sadasivan (1987) and method of averages by Kveton (1987).…”

Section: For General Discussion)mentioning

confidence: 99%

L1‎‎ Norm Based Data Analysis and Related Methods

Bidabad

2019

afbr

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Section: For General Discussion)mentioning

confidence: 99%

L1‎‎ Norm Based Data Analysis and Related Methods

Bidabad

2019

afbr

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“…After that different authors suggested different compromise criteria or explored further the already existing criteria. Among them are [26], [6], [16], [20], [10], [11], [7], [2], [3], [24], [8], [9], [23], [17], [18], [19], [14], [15], [5] and many others. [21] discussed five different compromise criteria to work out approximate optimum allocation in multivariate surveys and compared them using a simulation study.…”

Section: Introductionmentioning

confidence: 99%

Compromise Mixed Allocation in Multivariate Stratified Sampling Using Dynamic Programming Technique

Ansari¹,

Varshney²,

Ahsan³

2018

JAS

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The idea of "Mixed Allocation" in stratified sampling was introduced by [4]. The concept was further developed by several authors in different manner. In the present paper the authors worked out the "Compromise Mixed Allocation" for multivariate stratified sampling for more than one. Say "p" characteristics using Dynamic Programming Technique are defined on each population unit. It is assumed that the properties of the strata on which the grouping scheme of [4] is based are prevalent in the multivariate case also. Numerical examples are also presented to illustrate the computational details.

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“…Various authors suggested different compromise criteria or explored further the already existing criteria. Among them are Dalenius (1957), Ghosh (1958), Yates (1960), Aoyama (1963), Folks and Antle (1965), Kokan and Khan (1967), Chatterjee (1967Chatterjee ( , 1968, Arvanitis and Afonja (1971), Khan (1977, 1982), Melaku and Sadasivan (1987), Bankier (1988), Bethel (1989), Kreienbrock (1993), Jahan et al (1994), Khan et al (1997), Khan et al (2003) , and many others. Kozak (2006a) gave three different compromise criteria and modified the random search method to develop an algorithm to obtain the compromise allocation for multivariate stratified populations.…”

Section: Introductionmentioning

confidence: 99%

An Optimum Multivariate-Multiobjective Stratified Sampling Design: Fuzzy Programming Approach

Varshney¹,

Gupta²,

Ali³

2017

Pak.j.stat.oper.res.

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In stratified sampling design when the cost of measuring the units is not significant in each stratum, the estimation of population mean or total constructed from a selected sample according to Neyman allocation is advisable. In general the practical use of Neyman allocation suffers from a number of limitations, when there is no information about strata standard deviations except about the equality of standard deviations between some of the strata, then the precision of the estimate may be increased by pooling the strata with equal standard deviations as a single stratum and the problem of allocation is resolved by using Neyman and proportional allocations simultaneously. In this paper the case of multiple pooling of the standard deviations of the estimates in a multivariate stratified sampling for more than three strata. The problem is formulated as a Multiobjective Nonlinear Programming Problem and its solution procedure is suggested by using Fuzzy Programming approach.

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and other methods for sample allocation in multivariate stratified surveys

Cited by 7 publications

References 12 publications

L1‎‎ Norm Based Data Analysis and Related Methods

L1‎‎ Norm Based Data Analysis and Related Methods

Compromise Mixed Allocation in Multivariate Stratified Sampling Using Dynamic Programming Technique

An Optimum Multivariate-Multiobjective Stratified Sampling Design: Fuzzy Programming Approach

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