Multivariate optimal allocation with box-constraints

Modern surveys aim at fostering accurate information on demographic and other variables. The necessity for providing figures on regional levels and on a variety of subclasses leads to fine stratifications of the population. Optimizing the accuracy of stratified random samples requires incorporating...

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Bibliographic Details
Main Authors: Ulf Friedrich, Ralf Münnich, Martin Rupp
Format: Article
Language:English
Published: Austrian Statistical Society 2018-02-01
Series:Austrian Journal of Statistics
Online Access:http://www.ajs.or.at/index.php/ajs/article/view/764
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author Ulf Friedrich
Ralf Münnich
Martin Rupp
author_facet Ulf Friedrich
Ralf Münnich
Martin Rupp
author_sort Ulf Friedrich
collection DOAJ
description Modern surveys aim at fostering accurate information on demographic and other variables. The necessity for providing figures on regional levels and on a variety of subclasses leads to fine stratifications of the population. Optimizing the accuracy of stratified random samples requires incorporating a vast amount of strata on various levels of aggregation. Accounting for several variables of interest for the optimization yields a multivariate optimal allocation problem in which practical issues such as cost restrictions or control of sampling fractions have to be considered. Taking advantage of the special structure of the variance functions and applying Pareto optimization, efficient algorithms are developed which allow solving large-scale problems. Additionally, integrality- and box-constraints on the sample sizes are considered. The performance of the algorithms is presented comparatively using an open household dataset illustrating their advantages and relevance for modern surveys.
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spelling doaj.art-5017fdf942a943a1a6f4616e8642289b2022-12-21T19:49:03ZengAustrian Statistical SocietyAustrian Journal of Statistics1026-597X2018-02-0147210.17713/ajs.v47i2.764Multivariate optimal allocation with box-constraintsUlf FriedrichRalf MünnichMartin RuppModern surveys aim at fostering accurate information on demographic and other variables. The necessity for providing figures on regional levels and on a variety of subclasses leads to fine stratifications of the population. Optimizing the accuracy of stratified random samples requires incorporating a vast amount of strata on various levels of aggregation. Accounting for several variables of interest for the optimization yields a multivariate optimal allocation problem in which practical issues such as cost restrictions or control of sampling fractions have to be considered. Taking advantage of the special structure of the variance functions and applying Pareto optimization, efficient algorithms are developed which allow solving large-scale problems. Additionally, integrality- and box-constraints on the sample sizes are considered. The performance of the algorithms is presented comparatively using an open household dataset illustrating their advantages and relevance for modern surveys.http://www.ajs.or.at/index.php/ajs/article/view/764
spellingShingle Ulf Friedrich
Ralf Münnich
Martin Rupp
Multivariate optimal allocation with box-constraints
Austrian Journal of Statistics
title Multivariate optimal allocation with box-constraints
title_full Multivariate optimal allocation with box-constraints
title_fullStr Multivariate optimal allocation with box-constraints
title_full_unstemmed Multivariate optimal allocation with box-constraints
title_short Multivariate optimal allocation with box-constraints
title_sort multivariate optimal allocation with box constraints
url http://www.ajs.or.at/index.php/ajs/article/view/764
work_keys_str_mv AT ulffriedrich multivariateoptimalallocationwithboxconstraints
AT ralfmunnich multivariateoptimalallocationwithboxconstraints
AT martinrupp multivariateoptimalallocationwithboxconstraints