Linear Regression With Nested Errors Using Probability-Linked Data

Samart, Klairung; Chambers, Ray

doi:10.1111/anzs.12052

Cited by 14 publications

(33 citation statements)

References 3 publications

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“…andf (2) are the means of f i 's and their squares respectively. Finally, K is a function of the domain sizes and the vector of λ's (Samart and Chambers [21]).…”

Section: Small Area Estimation Based On Unit Linear Mixed Modelmentioning

confidence: 99%

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New methods for small area estimation with linkage uncertainty

Briscolini

Consiglio

Liseo

et al. 2018

International Journal of Approximate Reasoning

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In Official Statistics, interest for data integration has been increasingly growing, due to the need of extracting information from different sources. However, the effects of these procedures on the validity of the resulting statistical analyses has been disregarded for a long time. In recent years, it has been largely recognized that linkage is not an error-free procedure and linkage errors, as false links and/or missed links, can invalidate the reliability of estimates in standard statistical models. In this paper we consider the general problem of making inference using data that have been probabilistically linked and we explore the effect of potential linkage errors on the production of small area estimates. We describe the existing methods and propose and compare new approaches both from a classical and from a Bayesian perspective. We perform a simulation study to assess pros and cons of each proposed method; our simulation scheme aims at reproducing a realistic context both for small area estimation and record linkage procedures.

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“…andf (2) are the means of f i 's and their squares respectively. Finally, K is a function of the domain sizes and the vector of λ's (Samart and Chambers [21]).…”

Section: Small Area Estimation Based On Unit Linear Mixed Modelmentioning

confidence: 99%

“…In general, there are no closed form expressions for the variance component estimators. Samart and Chambers [21] use the method of scoring as an algorithm to obtain the estimators. In the standard case, i.e.…”

Section: Estimation Of Variance Components (Ml)mentioning

confidence: 99%

New methods for small area estimation with linkage uncertainty

Briscolini

Consiglio

Liseo

et al. 2018

International Journal of Approximate Reasoning

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“…In the context of fitting mixed models with linked data, Samart and Chambers (2014) extend the settings in Chambers (2009) and suggest linkage error adjusted estimators of variance effects under alternative methods. In Official Statistics, mixed models are largely used for small area estimation to increase the detail of dissemination of statistical information at local level.…”

Section: Data Integration and The Impact Of Linkage Errorsmentioning

confidence: 92%

A comparison of area level and unit level small area models in the presence of linkage errors

Consiglio

Tuoto

2020

Statistics in Transition New Series

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In Official Statistics, interest in data integration has grown enormously, but the effect of integration procedures on statistical analysis has not yet been sufficiently developed. Data integration is not an error-free procedure and linkage errors, as false links and missed links can invalidate standard estimates. Recently, increasing attention has been paid to the effect of linkage errors on the statistical analyses and on statistical predictions. Recently, methods to adjust the unit level small area estimators for linkage errors have been proposed when the domains are correctly specified. In this paper we compare the naïve and the adjusted unit level estimators with the area level estimators that are not affected by the linkage errors. The comparison encourages the use of the adjusted unit level estimator.

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“…More recently, following Neter et al (1965), Chambers (2009) put forward a variety of methods for different secondary data analyses that uses a sample to correct for linkage error biases. Following the work of Chambers (2009), researchers advanced the secondary data analysis of linked data in several different directions; see Chambers et al (2009), Chipperfield et al (2011, Kim and Chambers (2012a, 2012b, 2013, Samart and Chambers (2014), Dasylva (2014), Chipperfield and Chambers (2015), and Chambers and Kim (2016). Kandari and Lahiri (2016), following up on Lahiri (1996), suggested a theory for predicting a function of misclassified binary variables using information from a sample.…”

Section: Introductionmentioning

confidence: 99%

Statistical Analysis with Linked Data

Han

Lahiri

2018

Int Statistical Rev

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Summary Computerised Record Linkage methods help us combine multiple data sets from different sources when a single data set with all necessary information is unavailable or when data collection on additional variables is time consuming and extremely costly. Linkage errors are inevitable in the linked data set because of the unavailability of error‐free unique identifiers. A small amount of linkage errors can lead to substantial bias and increased variability in estimating parameters of a statistical model. In this paper, we propose a unified theory for statistical analysis with linked data. Our proposed method, unlike the ones available for secondary data analysis of linked data, exploits record linkage process data as an alternative to taking a costly sample to evaluate error rates from the record linkage procedure. A jackknife method is introduced to estimate bias, covariance matrix and mean squared error of our proposed estimators. Simulation results are presented to evaluate the performance of the proposed estimators that account for linkage errors.

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Linear Regression With Nested Errors Using Probability-Linked Data

Cited by 14 publications

References 3 publications

New methods for small area estimation with linkage uncertainty

New methods for small area estimation with linkage uncertainty

A comparison of area level and unit level small area models in the presence of linkage errors

Statistical Analysis with Linked Data

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