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2. Small Area Estimation with Linked Data

Author

Chambers, Ray, Fabrizi, Enrico, and Salvati, Nicola

Subjects
Statistics - Methodology
Abstract

In Small Area Estimation data linkage can be used to combine values of the variableof interest from a national survey with values of auxiliary variables obtained from another source like a population register. Linkage errors can induce bias when fitting regression models; moreover, they can create non-representative outliers in the linked data in addition to the presence of potential representative outliers. In this paper we adopt a secondary analyst's point view, assuming limited information is available on the linkage process, and we develop small area estimators based on linear mixed and linear M-quantile models to accommodate linked data containing a mix of both types of outliers. We illustrate the properties of these small area estimators, as well as estimators of their mean squared error, by means of model-based and design-based simulation experiments. These experiments show that the proposed predictors can lead to more efficient estimators when there is linkage error. Furthermore, the proposed mean-squared error estimation methods appear to perform well., Comment: 29 pages, 3 Figures

Published
2019

4. Disease Mapping via Negative Binomial Regression M-quantiles

Author

Chambers, Ray, Dreassi, Emanuela, and Salvati, Nicola

Subjects
Statistics - Methodology
Abstract

We introduce a semi-parametric approach to ecological regression for disease mapping, based on modelling the regression M-quantiles of a Negative Binomial variable. The proposed method is robust to outliers in the model covariates, including those due to measurement error, and can account for both spatial heterogeneity and spatial clustering. A simulation experiment based on the well-known Scottish lip cancer data set is used to compare the M-quantile modelling approach and a random effects modelling approach for disease mapping. This suggests that the M-quantile approach leads to predicted relative risks with smaller root mean square error than standard disease mapping methods. The paper concludes with an illustrative application of the M-quantile approach, mapping low birth weight incidence data for English Local Authority Districts for the years 2005-2010., Comment: 23 pages, 7 figures

Published
2013
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16. Predicting the Finite Population Distribution Function under a Multilevel Model

Author

Das, Sumonkanti, Salvati, Nicola, Chambers, Ray, Lahiri, Partha, Bonnery, Daniel, Cirillo, Cinzia, Fabrizi, Enrico, Franco, Carolina, Gershunskaya, Julie, and Li, Yan

Abstract

Chambers and Dunstan proposed a model-based predictor of the population distribution function that makes use of auxiliary population information under a general sampling design. Subsequently, Rao, Kovar, and Mantel proposed design-based ratio and difference predictors of the population distribution function that also use this auxiliary information. Both predictors (CD and RKM) assume a single level model for the target population. In this article we develop predictors of the finite population distribution function for a population that follows a multilevel model. These new predictors use the same smearing approach underpinning the CD predictor. We compare our new predictors with the CD and RKM predictors via design-based simulation, and show that they perform better than these single level predictors when there is significant intra-cluster correlation. The performances of these new two level predictors are also examined via an empirical study based on data from a large-scale UK business survey aimed at estimating the distribution of hourly pay rates.AMS Subject Classification:Primary 62G30, Secondary 62G32

Published
2023
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31. Christopher John Skinner, 1953–2020

Author

Chambers, Ray, primary, Diamond, Ian, additional, Holt, Tim, additional, Kuha, Jouni, additional, Pfeffermann, Danny, additional, Shlomo, Natalie, additional, Silva, Pedro Nascimento, additional, Smith, Paul, additional, Steel, David, additional, and Steele, Fiona, additional

Published
2020
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36. Discussion

Author

Chambers Ray

Subjects
Statistics, HA1-4737
Published
2015

37. Improved secondary analysis of linked data: a framework and an illustration.

Author

Chambers, Ray and Diniz da Silva, Andrea

Subjects
SECONDARY analysis, SOCIAL science research, AGRICULTURAL industries, DATABASES, GROWTH industries, REGRESSION trees
Abstract

Summary: Applications that use linked data are now part of mainstream social science research, though they generally do not take linkage error into consideration. Solutions that correct for the bias caused by these errors have been proposed but are not yet embedded in the various analysis procedures in common use. Secondary analyses based on linked data can therefore be potentially misleading. We review some recent approaches to non‐deterministic data linkage together with a framework for secondary analysis of the linked data which makes use of paradata produced by the linkage process to correct this bias. We also describe a new method for secondary analysis of linked data that builds on this framework and show how it can be used for estimation of a set of domain means based on linked data. We then illustrate this approach via an empirical study based on record linkage of agricultural producers in four states of Brazil aimed at producing estimates of agricultural output by industry. Our study considers register‐to‐register linkage as well as sample‐to‐register linkage, and we show results for the traditional Fellegi–Sunter approach to record linkage as well as for a newer linkage procedure based on the use of classification trees and bagging. [ABSTRACT FROM AUTHOR]

Published
2020
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41. Outlier robust small area estimation under spatial correlation

Author

Schmid, Timo, Tzavidis, Nikos, Munnich, Ralf, and Chambers, Ray

Subjects
bias correction,projective and predictive estimators,spatial correlation,business surveys, spatial correlation, business surveys, 300 Sozialwissenschaften::310 Statistiken, ddc:330, Bias correction, projective and predictive estimators, bias correction
Abstract

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.

Published
2015

42. A Role for Public-Private Partnerships

Author

Downey, Mortimer and Chambers, Ray

Subjects
Public-private sector cooperation -- Economic aspects, Company financing, Business, Transportation industry
Abstract

BYLINE: Mortimer Downey and Ray Chambers There is a growing discussion among elected officials, transportation interests and communities concerning the appropriate role for public-private partnerships in financing transportation projects.  This [...]

Published
2007

47. The industry should speak with one voice

Author

Chambers, Ray B.

Subjects
Railroads and state -- Analysis, Transportation policy -- Analysis
Abstract

Why should passenger railroads support H.R. 1020, the Railroad Track Modernization Act of 2001, a bill designed to assist short line and regional freight carriers? It has nothing to do […]

Published
2001

50. Using social network information for survey estimation

Author

Suesse, Thomas, Chambers, Ray, Suesse, Thomas, and Chambers, Ray

Abstract

Model-based and model-assisted methods of survey estimation aim to improve the precision of estimators of the population total or mean relative to methods based on the nonparametric Horvitz-Thompson estimator. These methods often use a linear regression model defined in terms of auxiliary variables whose values are assumed known for all population units. Information on networks represents another form of auxiliary information that might increase the precision of these estimators, particularly if it is reasonable to assume that networked population units have similar values of the survey variable. Linear models that use networks as a source of auxiliary information include autocorrelation, disturbance and contextual models. In this paper we focus on social networks, and investigate how much of the population structure of the network needs to be known for estimation methods based on these models to be useful. In particular, we use simulation to compare the performance of the best linear unbiased predictor under a model that ignores the network with model-based estimators that incorporate network information. Our results show that incorporating network information via a contextual model seems to be the most appropriate approach. We also show that one does not need to know the full population network, but that knowledge of the partial network linking the sampled population units to the non-sampled population units is necessary.

Published
2014

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