A unified approach to robust estimation in finite population sampling

Beaumont, Jean-François; Haziza, David; Ruiz‐Gazen, Anne

doi:10.1093/biomet/ast010

Cited by 26 publications

(45 citation statements)

References 12 publications

(14 reference statements)

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“…The synthetic estimator requires knowledge of N b from a census, large reference survey, or similar source. The new method is related to methods of weight trimming and Winsorization, which are well known and commonly practiced in survey research …”

Section: Methods Of Estimationmentioning

confidence: 99%

Estimation tools for reducing the impact of sampling and nonresponse errors in dual‐frame RDD telephone surveys

Wolter

Ganesh

Copeland

et al. 2019

Statistics in Medicine

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We discuss alternative estimators of the population total given a dual‐frame random‐digit‐dial (RDD) telephone survey in which samples are selected from landline and cell phone sampling frames. The estimators are subject to sampling and nonsampling errors. To reduce sampling variability when an optimum balance of landline and cell phone samples is not feasible, we develop an application of shrinkage estimation. We demonstrate the implications for survey weighting of a differential nonresponse mechanism by telephone status. We illustrate these ideas using data from the National Immunization Survey‐Child, a large dual‐frame RDD telephone survey sponsored by the Centers for Disease Control and Prevention and conducted to measure the vaccination status of American children aged 19 to 35 months.

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Section: Methods Of Estimationmentioning

confidence: 99%

Estimation tools for reducing the impact of sampling and nonresponse errors in dual‐frame RDD telephone surveys

Wolter

Ganesh

Copeland

et al. 2019

Statistics in Medicine

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“…In this section, we carry out Monte Carlo simulations to explore the finite‐sample performance of the proposed bootstrap procedure for estimating the MSE. For this purpose, we consider the EBLUP and three small‐area robust estimators: the robust estimator of Sinha and Rao (), SR; the robust estimator of Chambers et al (), CCST3; and the robust estimator of Jiongo et al () based on the conditional bias concept of Beaumont, Haziza, and Ruiz‐Gazen (), JHD (see the Supplementary Material for details).…”

Section: Monte Carlo Simulationsmentioning

confidence: 99%

Bootstrapping mean‐squared errors of robust small‐area estimators: Application to the method‐of‐payments surveys data

Jiongo

Nguimkeu

2019

Scandinavian J Statistics

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This paper proposes a new bootstrap procedure for mean‐squared errors of robust small‐area estimators. We formally prove the asymptotic validity of the proposed bootstrap method and examine its finite‐sample performance through Monte Carlo simulations. The results show that our procedure performs well and competes with existing ones. We also provide an application to the estimation of the total volume and value of cash, debit card, and credit card transactions in Canada as well as in its provinces and subgroups of households. In particular, we found that there is a significant average annual decline rate of 3.1% in the volume of cash transactions and that this decline is relatively higher among high‐income households living in heavily populated provinces. Our bootstrap estimator also provides indicators of quality useful in selecting the best small‐area predictor among several alternatives in practice.

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“…It is defined by

B_{j} ((), y_{j}, v_{h}, bold-italicβ, bold-italicθ) = E ((), {\overset{\overset{y}{false}}{true}}_{i} (bold-italicθ) - {\overset{y}{false}}_{i} | s, y_{j}, v_{h})

of a unit j in an area h (cf. Beaumont et al , ).…”

Section: Outlier Robust Small‐area Estimationmentioning

confidence: 99%

Outlier Robust Small‐Area Estimation Under Spatial Correlation

Schmid

Tzavidis

Münnich

et al. 2016

Scandinavian J Statistics

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Modern systems of official statistics require the estimation and publication of business statistics for disaggregated domains, for example, industry domains and geographical regions. Outlier robust methods have proven to be useful for small-area estimation. Recently proposed outlier robust model-based small-area methods assume, however, uncorrelated random effects. Spatial dependencies, resulting from similar industry domains or geographic regions, often occur. In this paper, we propose an outlier robust small-area methodology that allows for the presence of spatial correlation in the data. In particular, we present a robust predictive methodology that incorporates the potential spatial impact from other areas (domains) on the small area (domain) of interest. We further propose two parametric bootstrap methods for estimating the mean-squared error. Simulations indicate that the proposed methodology may lead to efficiency gains. The paper concludes with an illustrative application by using business data for estimating average labour costs in Italian provinces. resulting from similar industry domains or geographic regions, often occur. In this paper we propose outlier robust small area methodology that allows for the presence of spatial correlation in the data. In particular, we present a robust predictive methodology that incorporates the potential spatial impact from other areas (domains) on the small area (domain) of interest. We further propose two parametric bootstrap methods for estimating the mean-squared error. Simulations indicate that the proposed methodology may lead to efficiency gains. The paper concludes with an illustrative application by using business data for estimating average labour costs in Italian provinces.

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A unified approach to robust estimation in finite population sampling

Cited by 26 publications

References 12 publications

Estimation tools for reducing the impact of sampling and nonresponse errors in dual‐frame RDD telephone surveys

Estimation tools for reducing the impact of sampling and nonresponse errors in dual‐frame RDD telephone surveys

Bootstrapping mean‐squared errors of robust small‐area estimators: Application to the method‐of‐payments surveys data

Outlier Robust Small‐Area Estimation Under Spatial Correlation

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