Evaluations of small area composite estimators based on the iterative proportional fitting algorithm

Angelo Moretti; Adam Whitworth

doi:10.1080/03610918.2018.1535067

Evaluations of small area composite estimators based on the iterative proportional fitting algorithm

Angelo Moretti^*
, Adam Whitworth

^*Corresponding author for this work

University of Sheffield
Department of Geography, University of Sheffield, Sheffield, United Kingdom

Research output: Contribution to journal › Article › Academic › peer-review

Abstract

This article deals with the use of sample size dependent composite estimators in spatial microsimulation approaches for small area estimation. This approach has been applied to regression-based small area estimation approaches but never to our knowledge to spatial microsimulation approaches. In this paper, we extend the iterative proportional fitting (IPF) spatial microsimulation technique to small area composite estimators. Using a simulation study, we show both the impact of sample size and the gains from composite estimation to the mean squared error of IPF-based composite estimators. The target variable used is a binary variable reporting good health or bad health.

Original language	English
Pages (from-to)	3093-3110
Number of pages	18
Journal	Communications in Statistics: Simulation and Computation
Volume	49
Issue number	12
DOIs	https://doi.org/10.1080/03610918.2018.1535067
Publication status	Published - 2020
Externally published	Yes

Bibliographical note

Funding Information:
This research has been funded by the UK Economic and Social Research Council (ESRC) National Centre for Research Methods (NCRM) grant number ES/N011619/1.

Publisher Copyright:
© 2019 Taylor & Francis Group, LLC.

Funding

This research has been funded by the UK Economic and Social Research Council (ESRC) National Centre for Research Methods (NCRM) grant number ES/N011619/1.

Keywords

Composite estimator
IPF
Small area estimation
Spatial microsimulation
Synthetic estimator

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10.1080/03610918.2018.1535067

Cite this

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title = "Evaluations of small area composite estimators based on the iterative proportional fitting algorithm",

abstract = "This article deals with the use of sample size dependent composite estimators in spatial microsimulation approaches for small area estimation. This approach has been applied to regression-based small area estimation approaches but never to our knowledge to spatial microsimulation approaches. In this paper, we extend the iterative proportional fitting (IPF) spatial microsimulation technique to small area composite estimators. Using a simulation study, we show both the impact of sample size and the gains from composite estimation to the mean squared error of IPF-based composite estimators. The target variable used is a binary variable reporting good health or bad health.",

keywords = "Composite estimator, IPF, Small area estimation, Spatial microsimulation, Synthetic estimator",

author = "Angelo Moretti and Adam Whitworth",

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AU - Whitworth, Adam

N1 - Funding Information: This research has been funded by the UK Economic and Social Research Council (ESRC) National Centre for Research Methods (NCRM) grant number ES/N011619/1. Publisher Copyright: © 2019 Taylor & Francis Group, LLC.

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Y1 - 2020

N2 - This article deals with the use of sample size dependent composite estimators in spatial microsimulation approaches for small area estimation. This approach has been applied to regression-based small area estimation approaches but never to our knowledge to spatial microsimulation approaches. In this paper, we extend the iterative proportional fitting (IPF) spatial microsimulation technique to small area composite estimators. Using a simulation study, we show both the impact of sample size and the gains from composite estimation to the mean squared error of IPF-based composite estimators. The target variable used is a binary variable reporting good health or bad health.

AB - This article deals with the use of sample size dependent composite estimators in spatial microsimulation approaches for small area estimation. This approach has been applied to regression-based small area estimation approaches but never to our knowledge to spatial microsimulation approaches. In this paper, we extend the iterative proportional fitting (IPF) spatial microsimulation technique to small area composite estimators. Using a simulation study, we show both the impact of sample size and the gains from composite estimation to the mean squared error of IPF-based composite estimators. The target variable used is a binary variable reporting good health or bad health.

KW - Composite estimator

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KW - Small area estimation

KW - Spatial microsimulation

KW - Synthetic estimator

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JO - Communications in Statistics: Simulation and Computation

JF - Communications in Statistics: Simulation and Computation

IS - 12

ER -

Evaluations of small area composite estimators based on the iterative proportional fitting algorithm

Abstract

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Keywords

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