Comparison of Random Search Method and Genetic Algorithm for Stratification

Kozak, Marcin

doi:10.1080/03610918.2012.700364

Cited by 12 publications

(9 citation statements)

References 8 publications

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“…Since LH only works on univariate administrative data, strata can be identified as a set of disjoint intervals of the real line. Two approaches to find the boundaries of these intervals are found in Baillargeon and Rivest (2011): a model based approach used in the original Lavalle ´e and Hidiroglou (1988) paper, and a random search method proposed in Kozak (2004). Due to the excellent performance characteristics without model assumptions, the random search method was chosen for this example.…”

Section: Univariate Examplementioning

confidence: 99%

Optimal Stratification and Allocation for the June Agricultural Survey

Lisic

Sang

Zhu

et al. 2018

Journal of Official Statistics

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A computational approach to optimal multivariate designs with respect to stratification and allocation is investigated under the assumptions of fixed total allocation, known number of strata, and the availability of administrative data correlated with thevariables of interest under coefficient-of-variation constraints. This approach uses a penalized objective function that is optimized by simulated annealing through exchanging sampling units and sample allocations among strata. Computational speed is improved through the use of a computationally efficient machine learning method such as K-means to create an initial stratification close to the optimal stratification. The numeric stability of the algorithm has been investigated and parallel processing has been employed where appropriate. Results are presented for both simulated data and USDA's June Agricultural Survey. An R package has also been made available for evaluation.

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Section: Univariate Examplementioning

confidence: 99%

Optimal Stratification and Allocation for the June Agricultural Survey

Lisic

Sang

Zhu

et al. 2018

Journal of Official Statistics

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“…By efficiently combining the solutions to the overall problem to be found. This technique is widely applied in various fields such as algorithm design, artificial intelligence, economics and bioinformatics, providing an efficient approach to tackle complex problem into optimal solutions as given in (11). Now let us assume a fraction of problem defined in (11) for (𝑙 1 , 𝑚 1 ) < (𝐿, 𝑀)…”

Section: Procedures For Obtaining the Solution Using Dynamic Programmingmentioning

confidence: 99%

“…Addressing the multivariate stratification problem, [10] introduced an algorithm that utilizes a penalized objective function optimized via the Simulated Annealing technique. Another approach, presented in [11], introduces algorithms for stratifying asymmetric populations using power allocation to estimate sample sizes, [12] utilized Dynamic Programming and Neyman allocation to tackle the stratification issue, assuming a Weibull distribution for the stratification variable. Several R packages, including GA4 Stratification (https://cran.rproject.org/src/contrib/Archive/GA4Stratification/), stratify R ( [13]), and sample (available on the R CRAN), offer tools for stratification.…”

Section: Introductionmentioning

confidence: 99%

Optimum Strata Boundaries for Breast Cancer Patients Using Dynamic Programming Approach

Danish

2024

Preprint

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The study examines the determination of stratification points for two study variables within the framework of simple random sampling, particularly focusing on calculating the population mean using a closely associated variable. Utilizing a superpopulation model, the investigation seeks to derive minimal equations by minimizing aggregated variance with the assistance of the variables under scrutiny. In order to improve the accuracy of estimating population parameters within a stratified sampling framework, it is suggested in the article to establish optimal strata boundaries (OSB) instead of categorizing variables, the study focuses on continuous variables for analysis. The creation of homogeneous units within each stratum, facilitated by optimally constructed OSB, leads to more effective sample sizes (SS) and consequently enhances precision in estimation. However, it's noted that the OSB and SS may not remain optimal in cases where a fixed total sample size is mandated, particularly in survey designs constrained by a fixed budget. To address this challenge, the article outlines a methodology for calculating the OSB and SS under the condition that the survey's per-unit stratum measurement costs or its probability density function is understood. An empirical demonstration of a design-based stratification approach is showcased using breast cancer data, where the mean perimeter is estimated based on mean radius and mean texture. Furthermore, numerical examples are provided for hypothetical study variables following Cauchy and standard power distributions. The novel approach has been integrated into the revised stratifyR package and LINGO software. The research outcomes indicate that the newly introduced method demonstrates superior efficiency or comparable performance when aiming to refine the precision of population parameter estimations.

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“…The optimum stratification problem, related to the field of probability sampling [8], can be formulated according to two possible goals: (A) minimizing the variance of an estimator given a fixed sample size or (B) minimizing the sample size for a fixed level of precision. In the literature, most methods were developed aiming at the first goal [2,3,5,6,10,15,18,22,25,26,30,[37][38][39], while the second goal has been less studied [23,24,28,33,35]. This article's optimization problem consists of minimizing the total sample size, simultaneously satisfying the constraints of precision and minimum sample size of each stratum.…”

Section: Introductionmentioning

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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Comparison of Random Search Method and Genetic Algorithm for Stratification

Cited by 12 publications

References 8 publications

Optimal Stratification and Allocation for the June Agricultural Survey

Optimal Stratification and Allocation for the June Agricultural Survey

Optimum Strata Boundaries for Breast Cancer Patients Using Dynamic Programming Approach

Heuristic approach applied to the optimum stratification problem

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