Obtaining Strata Boundaries under Proportional Allocation  with Varying Cost of Every Unit

Danish, Faizan; Rizvi, S. E. H.; Jeelani, M. Iqbal; Reashi, Javaid Ahmad

doi:10.18187/pjsor.v13i3.1719

Cited by 12 publications

(12 citation statements)

References 9 publications

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“…As described in this section, Step 1 consists of defining of defining the objective function by either: (i) minimizing the total estimator variance, that is, maximizing the precision considering a fixed sample size; (ii) minimizing the sample size, that is, minimizing cost given a fixed precision. Most of the proposed methods in the literature are related to the objective function described in (i), as can be seen in [2,3,5,6,[10][11][12][13][14][15][16]18,22,25,26,[37][38][39][40]. The objective function described in (ii) was only studied by Hidiroglou [23], Lavallée and Hidiroglou [33], Kozak [28], Hidiroglou and Kozak [24] and Lisic et al [35].…”

Section: Optimal Stratification Problemmentioning

confidence: 99%

Heuristic approach applied to the optimum stratification problem

Brito¹,

Lima

González

et al. 2021

RAIRO-Oper. Res.

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The problem of finding an optimal sample stratification has been extensively studied in the literature. In this paper, we propose a heuristic optimization method for solving the univariate optimum stratification problem aiming at minimizing the sample size for a given precision level. The method is based on the variable neighborhood search metaheuristic, which was combined with an exact method. Numerical experiments were performed over a dataset of 24 instances, and the results of the proposed algorithm were compared with two very well-known methods from the literature. Our results outperformed $94\%$ of the considered cases. In addition, we developed an enumeration algorithm to find the global optimal solution in some populations and scenarios, which enabled us to validate our metaheuristic method. Furthermore, we find that our algorithm obtained the global optimal solutions for the vast majority of the cases.

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Section: Optimal Stratification Problemmentioning

confidence: 99%

Heuristic approach applied to the optimum stratification problem

Brito¹,

Lima

González

et al. 2021

RAIRO-Oper. Res.

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“…al. [ 9 ] presented optimum strata boundaries as a non-linear programming problem when the cost per unit varies throughout the strata. Reddy et.…”

Section: Introductionmentioning

confidence: 99%

Optimum stratum boundaries and sample sizes for Covid-19 data in Egypt

Abo_El.Hassan

Hamed²,

Ismail³

et al. 2022

PLoS ONE

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Stratified random sampling is an effective sampling technique for estimating the population characteristics. The determination of strata boundaries and the allocation of sample size to the strata are two of the most critical factors in maximizing the precision of the estimates. Most surveys are conducted in an environment of severe budget constraints and a specific time is required to finish the survey. So cost and time are two important objectives that are taken under consideration in most surveys. The study suggested Mathematical goal programming model for determining optimum stratum boundaries for an exponential study variable under multiple objectives model when cost and time are under consideration. Compared to other techniques, Goal programming has many advantages in resources planning. Determining the required resources to satisfy the desired goals and the effectiveness of the available resources as well as providing best solutions under different amounts of resources are examples of the advantages of Goal programming. In addition the paper used data on Covid-19 to evaluate the performance of the suggested model for the exponential distribution. The study divided the number of new cases diseases into small, medium and high numbers. It also compared the results with the findings in the reports of the World Health Organization. The suggested mathematical goal programming revealed that Egypt was exposed to three waves of infection during the interval (5/3/2020 to 12/8/2021). These results are identical to the actual reality of covid-19 waves in Egypt.

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“…Rizvi, et al (2002) [10] developed a methodology for obtaining AOSB using auxiliary information under compromise method of allocation. Danish, et al (2017) [3] studied the problem of obtaining OSB in proportional allocation by changing cost of each unit. Danish and Rizvi (2018) [4] proposed a technique under Neyman allocation and recently, Gupt et al (2022) [6] considered problem of optimum stratification of heteroscedastic populations in stratified sampling for a known allocation under SRS strategy.…”

Section: Introductionmentioning

confidence: 99%

Optimum stratification for equal allocation using ranked set sampling as a method of selection

Rather¹,

Rizvi²,

Sharma³

et al. 2022

Int. J. Stat. Appl. Math.

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Present investigation deals with the problem of optimum stratification on the auxiliary variable for equal allocation when method of selection of units in individual strata is done by ranked set sampling (RSS). A ) ( 4 x K cum rule of obtaining approximate optimum strata boundaries has been developed by minimizing the variance and solution of minimal equations thereof. A numerical analysis on the basis of percent relative efficiency has also been made, which indicated remarkable gain in efficiency.

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Obtaining Strata Boundaries under Proportional Allocation with Varying Cost of Every Unit

Cited by 12 publications

References 9 publications

Heuristic approach applied to the optimum stratification problem

Heuristic approach applied to the optimum stratification problem

Optimum stratum boundaries and sample sizes for Covid-19 data in Egypt

Optimum stratification for equal allocation using ranked set sampling as a method of selection

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