<abstract xmlns="http://www.w3.org/1999/xhtml"><p> We consider the problem of constructing a synthetic sample from a population of interest which cannot be sampled from but for which the population means of some of its variables are known. In addition, we assume that we have in hand samples from two similar populations. Using the known population means, we will select subsamples from the samples of the other two populations which we will then combine to construct the synthetic sample. The synthetic sample is obtained by solving an optimization problem, where the known population means, are used as constraints. The optimization is achieved through an adaptive random search algorithm. Simulation studies are presented to demonstrate the effectiveness of our approach. We observe that on average, such synthetic samples behave very much like actual samples from the population of interest. As an application we consider constructing a one-percent synthetic sample for the missing 1890 decennial sample of the United States.</p></abstract>

We consider the problem of constructing a synthetic sample from a population of interest which cannot be sampled from but for which the population means of some of its variables are known. In addition, we assume that we have in hand samples from two similar populations. Using the known population means, we will select subsamples from the samples of the other two populations which we will then combine to construct the synthetic sample. The synthetic sample is obtained by solving an optimization problem, where the known population means, are used as constraints. The optimization is achieved through an adaptive random search algorithm. Simulation studies are presented to demonstrate the effectiveness of our approach. We observe that on average, such synthetic samples behave very much like actual samples from the population of interest. As an application we consider constructing a one-percent synthetic sample for the missing 1890 decennial sample of the United States.

Constructing Synthetic Samples

Gilead Sciences, Inc. Foster City, CA 94404, U.S.A.

School of Statistics, University of Minnesota, Minneapolis, MN 55455, U.S.A.

Journal of Official Statistics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"Constructing Synthetic Samples"}

We consider the problem of constructing a synthetic sample from a population of interest which cannot be sampled from but for which the population means of...

Constructing Synthetic Samples

Pubblicato online: 10 mar 2016

Pagine: 113 - 127

Ricevuto: 01 gen 2013

Accettato: 01 nov 2015

DOI: https://doi.org/10.1515/jos-2016-0005

Parole chiave
Sample survey, missing data, synthetic samples

© by Hua Dong

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Constructing Synthetic Samples

Pubblicato online: 10 mar 2016

Pagine: 113 - 127

Ricevuto: 01 gen 2013

Accettato: 01 nov 2015

DOI: https://doi.org/10.1515/jos-2016-0005

Parole chiaveSample survey, missing data, synthetic samples

© by Hua Dong

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Parole chiave
Sample survey, missing data, synthetic samples