A simulated annealing-based algorithm for selecting balanced samples

被引：1

作者：

Benedetti, Roberto ^{[1
]}

Dickson, Maria Michela ^{[2
]}

Espa, Giuseppe ^{[2
]}

Pantalone, Francesco ^{[3
]}

Piersimoni, Federica ^{[4
]}

机构：

[1] Univ G dAnnunzio, Dept Econ Studies, I-65127 Pescara, Italy

[2] Univ Trento, Dept Econ & Management, I-38122 Trento, Italy

[3] Univ Perugia, Dept Econ, I-06123 Perugia, Italy

[4] Istat, Directorate Methodol & Stat Proc Design, I-00184 Rome, Italy

来源：

COMPUTATIONAL STATISTICS | 2022年 / 37卷 / 01期

关键词：

Balanced sampling; Auxiliary variables; Sampling algorithms; Simulated annealing; DESIGNS;

D O I：

10.1007/s00180-021-01113-3

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

Balanced sampling is a random method for sample selection, the use of which is preferable when auxiliary information is available for all units of a population. However, implementing balanced sampling can be a challenging task, and this is due in part to the computational efforts required and the necessity to respect balancing constraints and inclusion probabilities. In the present paper, a new algorithm for selecting balanced samples is proposed. This method is inspired by simulated annealing algorithms, as a balanced sample selection can be interpreted as an optimization problem. A set of simulation experiments and an example using real data shows the efficiency and the accuracy of the proposed algorithm.

引用

页码：491 / 505

页数：15

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