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All (112) (0 to 10 of 112 results)

1. A proposal for the problem of matching probabilities estimation in record linkage Archived
Articles and reports: 11-522-X202200100001
Description: Record linkage aims at identifying record pairs related to the same unit and observed in two different data sets, say A and B. Fellegi and Sunter (1969) suggest each record pair is tested whether generated from the set of matched or unmatched pairs. The decision function consists of the ratio between m(y) and u(y),probabilities of observing a comparison y of a set of k>3 key identifying variables in a record pair under the assumptions that the pair is a match or a non-match, respectively. These parameters are usually estimated by means of the EM algorithm using as data the comparisons on all the pairs of the Cartesian product ?=A×B. These observations (on the comparisons and on the pairs status as match or non-match) are assumed as generated independently of other pairs, assumption characterizing most of the literature on record linkage and implemented in software tools (e.g. RELAIS, Cibella et al. 2012). On the contrary, comparisons y and matching status in ? are deterministically dependent. As a result, estimates on m(y) and u(y) based on the EM algorithm are usually bad. This fact jeopardizes the effective application of the Fellegi-Sunter method, as well as automatic computation of quality measures and possibility to apply efficient methods for model estimation on linked data (e.g. regression functions), as in Chambers et al. (2015). We propose to explore ? by a set of samples, each one drawn so to preserve independence of comparisons among the selected record pairs. Simulations are encouraging.
Release date: 2024-03-25
2. Integration of existing data to develop an ethnicity indicator in the LSDDP Archived
Articles and reports: 11-522-X202200100018
Description: The Longitudinal Social Data Development Program (LSDDP) is a social data integration approach aimed at providing longitudinal analytical opportunities without imposing additional burden on respondents. The LSDDP uses a multitude of signals from different data sources for the same individual, which helps to better understand their interactions and track changes over time. This article looks at how the ethnicity status of people in Canada can be estimated at the most detailed disaggregated level possible using the results from a variety of business rules applied to linked data and to the LSDDP denominator. It will then show how improvements were obtained using machine learning methods, such as decision trees and random forest techniques.
Release date: 2024-03-25
3. A method for estimating the effect of classification errors on statistics for two domains
Articles and reports: 12-001-X202300200002
Description: Being able to quantify the accuracy (bias, variance) of published output is crucial in official statistics. Output in official statistics is nearly always divided into subpopulations according to some classification variable, such as mean income by categories of educational level. Such output is also referred to as domain statistics. In the current paper, we limit ourselves to binary classification variables. In practice, misclassifications occur and these contribute to the bias and variance of domain statistics. Existing analytical and numerical methods to estimate this effect have two disadvantages. The first disadvantage is that they require that the misclassification probabilities are known beforehand and the second is that the bias and variance estimates are biased themselves. In the current paper we present a new method, a Gaussian mixture model estimated by an Expectation-Maximisation (EM) algorithm combined with a bootstrap, referred to as the EM bootstrap method. This new method does not require that the misclassification probabilities are known beforehand, although it is more efficient when a small audit sample is used that yields a starting value for the misclassification probabilities in the EM algorithm. We compared the performance of the new method with currently available numerical methods: the bootstrap method and the SIMEX method. Previous research has shown that for non-linear parameters the bootstrap outperforms the analytical expressions. For nearly all conditions tested, the bias and variance estimates that are obtained by the EM bootstrap method are closer to their true values than those obtained by the bootstrap and SIMEX methods. We end this paper by discussing the results and possible future extensions of the method.
Release date: 2024-01-03
4. Comments by Carl-Erik Särndal on “Jean-Claude Deville’s contributions to survey theory and official statistics”
Articles and reports: 12-001-X202300200012
Description: In recent decades, many different uses of auxiliary information have enriched survey sampling theory and practice. Jean-Claude Deville contributed significantly to this progress. My comments trace some of the steps on the way to one important theory for the use of auxiliary information: Estimation by calibration.
Release date: 2024-01-03
5. Combining data from surveys and related sources
Articles and reports: 12-001-X202300100003
Description: To improve the precision of inferences and reduce costs there is considerable interest in combining data from several sources such as sample surveys and administrative data. Appropriate methodology is required to ensure satisfactory inferences since the target populations and methods for acquiring data may be quite different. To provide improved inferences we use methodology that has a more general structure than the ones in current practice. We start with the case where the analyst has only summary statistics from each of the sources. In our primary method, uncertain pooling, it is assumed that the analyst can regard one source, survey r, as the single best choice for inference. This method starts with the data from survey r and adds data from those other sources that are shown to form clusters that include survey r. We also consider Dirichlet process mixtures, one of the most popular nonparametric Bayesian methods. We use analytical expressions and the results from numerical studies to show properties of the methodology.
Release date: 2023-06-30
6. Official Statistics based on the Dutch Health Survey during the Covid-19 Pandemic
Articles and reports: 12-001-X202300100004
Description: The Dutch Health Survey (DHS), conducted by Statistics Netherlands, is designed to produce reliable direct estimates at an annual frequency. Data collection is based on a combination of web interviewing and face-to-face interviewing. Due to lockdown measures during the Covid-19 pandemic there was no or less face-to-face interviewing possible, which resulted in a sudden change in measurement and selection effects in the survey outcomes. Furthermore, the production of annual data about the effect of Covid-19 on health-related themes with a delay of about one year compromises the relevance of the survey. The sample size of the DHS does not allow the production of figures for shorter reference periods. Both issues are solved by developing a bivariate structural time series model (STM) to estimate quarterly figures for eight key health indicators. This model combines two series of direct estimates, a series based on complete response and a series based on web response only and provides model-based predictions for the indicators that are corrected for the loss of face-to-face interviews during the lockdown periods. The model is also used as a form of small area estimation and borrows sample information observed in previous reference periods. In this way timely and relevant statistics describing the effects of the corona crisis on the development of Dutch health are published. In this paper the method based on the bivariate STM is compared with two alternative methods. The first one uses a univariate STM where no correction for the lack of face-to-face observation is applied to the estimates. The second one uses a univariate STM that also contains an intervention variable that models the effect of the loss of face-to-face response during the lockdown.
Release date: 2023-06-30
7. Attrition in the Longitudinal and International Study of Adults, Wave 1 (2012) to Wave 4 (2018)
Articles and reports: 89-648-X2022001
Description:
This report explores the size and nature of the attrition challenges faced by the Longitudinal and International Study of Adults (LISA) survey, as well as the use of a non-response weight adjustment and calibration strategy to mitigate the effects of attrition on the LISA estimates. The study focuses on data from waves 1 (2012) to 4 (2018) and uses practical examples based on selected demographic variables, to illustrate how attrition be assessed and treated.

Release date: 2022-11-14
8. Bayesian inference for a variance component model using pairwise composite likelihood with survey data
Articles and reports: 12-001-X202200100002
Description:
We consider an intercept only linear random effects model for analysis of data from a two stage cluster sampling design. At the first stage a simple random sample of clusters is drawn, and at the second stage a simple random sample of elementary units is taken within each selected cluster. The response variable is assumed to consist of a cluster-level random effect plus an independent error term with known variance. The objects of inference are the mean of the outcome variable and the random effect variance. With a more complex two stage sampling design, the use of an approach based on an estimated pairwise composite likelihood function has appealing properties. Our purpose is to use our simpler context to compare the results of likelihood inference with inference based on a pairwise composite likelihood function that is treated as an approximate likelihood, in particular treated as the likelihood component in Bayesian inference. In order to provide credible intervals having frequentist coverage close to nominal values, the pairwise composite likelihood function and corresponding posterior density need modification, such as a curvature adjustment. Through simulation studies, we investigate the performance of an adjustment proposed in the literature, and find that it works well for the mean but provides credible intervals for the random effect variance that suffer from under-coverage. We propose possible future directions including extensions to the case of a complex design.

Release date: 2022-06-21
9. Small area benchmarked estimation under the basic unit level model when the sampling rates are non-negligible
Articles and reports: 12-001-X202100100007
Description:
We consider the estimation of a small area mean under the basic unit-level model. The sum of the resulting model-dependent estimators may not add up to estimates obtained with a direct survey estimator that is deemed to be accurate for the union of these small areas. Benchmarking forces the model-based estimators to agree with the direct estimator at the aggregated area level. The generalized regression estimator is the direct estimator that we benchmark to. In this paper we compare small area benchmarked estimators based on four procedures. The first procedure produces benchmarked estimators by ratio adjustment. The second procedure is based on the empirical best linear unbiased estimator obtained under the unit-level model augmented with a suitable variable that ensures benchmarking. The third procedure uses pseudo-empirical estimators constructed with suitably chosen sampling weights so that, when aggregated, they agree with the reliable direct estimator for the larger area. The fourth procedure produces benchmarked estimators that are the result of a minimization problem subject to the constraint given by the benchmark condition. These benchmark procedures are applied to the small area estimators when the sampling rates are non-negligible. The resulting benchmarked estimators are compared in terms of relative bias and mean squared error using both a design-based simulation study as well as an example with real survey data.
Release date: 2021-06-24
10. Bayesian hierarchical weighting adjustment and survey inference
Articles and reports: 12-001-X202000200003
Description:
We combine weighting and Bayesian prediction in a unified approach to survey inference. The general principles of Bayesian analysis imply that models for survey outcomes should be conditional on all variables that affect the probability of inclusion. We incorporate all the variables that are used in the weighting adjustment under the framework of multilevel regression and poststratification, as a byproduct generating model-based weights after smoothing. We improve small area estimation by dealing with different complex issues caused by real-life applications to obtain robust inference at finer levels for subdomains of interest. We investigate deep interactions and introduce structured prior distributions for smoothing and stability of estimates. The computation is done via Stan and is implemented in the open-source R package rstanarm and available for public use. We evaluate the design-based properties of the Bayesian procedure. Simulation studies illustrate how the model-based prediction and weighting inference can outperform classical weighting. We apply the method to the New York Longitudinal Study of Wellbeing. The new approach generates smoothed weights and increases efficiency for robust finite population inference, especially for subsets of the population.
Release date: 2020-12-15

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Analysis (111)

Analysis (111) (0 to 10 of 111 results)

1. A proposal for the problem of matching probabilities estimation in record linkage Archived
Articles and reports: 11-522-X202200100001
Description: Record linkage aims at identifying record pairs related to the same unit and observed in two different data sets, say A and B. Fellegi and Sunter (1969) suggest each record pair is tested whether generated from the set of matched or unmatched pairs. The decision function consists of the ratio between m(y) and u(y),probabilities of observing a comparison y of a set of k>3 key identifying variables in a record pair under the assumptions that the pair is a match or a non-match, respectively. These parameters are usually estimated by means of the EM algorithm using as data the comparisons on all the pairs of the Cartesian product ?=A×B. These observations (on the comparisons and on the pairs status as match or non-match) are assumed as generated independently of other pairs, assumption characterizing most of the literature on record linkage and implemented in software tools (e.g. RELAIS, Cibella et al. 2012). On the contrary, comparisons y and matching status in ? are deterministically dependent. As a result, estimates on m(y) and u(y) based on the EM algorithm are usually bad. This fact jeopardizes the effective application of the Fellegi-Sunter method, as well as automatic computation of quality measures and possibility to apply efficient methods for model estimation on linked data (e.g. regression functions), as in Chambers et al. (2015). We propose to explore ? by a set of samples, each one drawn so to preserve independence of comparisons among the selected record pairs. Simulations are encouraging.
Release date: 2024-03-25
2. Integration of existing data to develop an ethnicity indicator in the LSDDP Archived
Articles and reports: 11-522-X202200100018
Description: The Longitudinal Social Data Development Program (LSDDP) is a social data integration approach aimed at providing longitudinal analytical opportunities without imposing additional burden on respondents. The LSDDP uses a multitude of signals from different data sources for the same individual, which helps to better understand their interactions and track changes over time. This article looks at how the ethnicity status of people in Canada can be estimated at the most detailed disaggregated level possible using the results from a variety of business rules applied to linked data and to the LSDDP denominator. It will then show how improvements were obtained using machine learning methods, such as decision trees and random forest techniques.
Release date: 2024-03-25
3. A method for estimating the effect of classification errors on statistics for two domains
Articles and reports: 12-001-X202300200002
Description: Being able to quantify the accuracy (bias, variance) of published output is crucial in official statistics. Output in official statistics is nearly always divided into subpopulations according to some classification variable, such as mean income by categories of educational level. Such output is also referred to as domain statistics. In the current paper, we limit ourselves to binary classification variables. In practice, misclassifications occur and these contribute to the bias and variance of domain statistics. Existing analytical and numerical methods to estimate this effect have two disadvantages. The first disadvantage is that they require that the misclassification probabilities are known beforehand and the second is that the bias and variance estimates are biased themselves. In the current paper we present a new method, a Gaussian mixture model estimated by an Expectation-Maximisation (EM) algorithm combined with a bootstrap, referred to as the EM bootstrap method. This new method does not require that the misclassification probabilities are known beforehand, although it is more efficient when a small audit sample is used that yields a starting value for the misclassification probabilities in the EM algorithm. We compared the performance of the new method with currently available numerical methods: the bootstrap method and the SIMEX method. Previous research has shown that for non-linear parameters the bootstrap outperforms the analytical expressions. For nearly all conditions tested, the bias and variance estimates that are obtained by the EM bootstrap method are closer to their true values than those obtained by the bootstrap and SIMEX methods. We end this paper by discussing the results and possible future extensions of the method.
Release date: 2024-01-03
4. Comments by Carl-Erik Särndal on “Jean-Claude Deville’s contributions to survey theory and official statistics”
Articles and reports: 12-001-X202300200012
Description: In recent decades, many different uses of auxiliary information have enriched survey sampling theory and practice. Jean-Claude Deville contributed significantly to this progress. My comments trace some of the steps on the way to one important theory for the use of auxiliary information: Estimation by calibration.
Release date: 2024-01-03
5. Combining data from surveys and related sources
Articles and reports: 12-001-X202300100003
Description: To improve the precision of inferences and reduce costs there is considerable interest in combining data from several sources such as sample surveys and administrative data. Appropriate methodology is required to ensure satisfactory inferences since the target populations and methods for acquiring data may be quite different. To provide improved inferences we use methodology that has a more general structure than the ones in current practice. We start with the case where the analyst has only summary statistics from each of the sources. In our primary method, uncertain pooling, it is assumed that the analyst can regard one source, survey r, as the single best choice for inference. This method starts with the data from survey r and adds data from those other sources that are shown to form clusters that include survey r. We also consider Dirichlet process mixtures, one of the most popular nonparametric Bayesian methods. We use analytical expressions and the results from numerical studies to show properties of the methodology.
Release date: 2023-06-30
6. Official Statistics based on the Dutch Health Survey during the Covid-19 Pandemic
Articles and reports: 12-001-X202300100004
Description: The Dutch Health Survey (DHS), conducted by Statistics Netherlands, is designed to produce reliable direct estimates at an annual frequency. Data collection is based on a combination of web interviewing and face-to-face interviewing. Due to lockdown measures during the Covid-19 pandemic there was no or less face-to-face interviewing possible, which resulted in a sudden change in measurement and selection effects in the survey outcomes. Furthermore, the production of annual data about the effect of Covid-19 on health-related themes with a delay of about one year compromises the relevance of the survey. The sample size of the DHS does not allow the production of figures for shorter reference periods. Both issues are solved by developing a bivariate structural time series model (STM) to estimate quarterly figures for eight key health indicators. This model combines two series of direct estimates, a series based on complete response and a series based on web response only and provides model-based predictions for the indicators that are corrected for the loss of face-to-face interviews during the lockdown periods. The model is also used as a form of small area estimation and borrows sample information observed in previous reference periods. In this way timely and relevant statistics describing the effects of the corona crisis on the development of Dutch health are published. In this paper the method based on the bivariate STM is compared with two alternative methods. The first one uses a univariate STM where no correction for the lack of face-to-face observation is applied to the estimates. The second one uses a univariate STM that also contains an intervention variable that models the effect of the loss of face-to-face response during the lockdown.
Release date: 2023-06-30
7. Attrition in the Longitudinal and International Study of Adults, Wave 1 (2012) to Wave 4 (2018)
Articles and reports: 89-648-X2022001
Description:
This report explores the size and nature of the attrition challenges faced by the Longitudinal and International Study of Adults (LISA) survey, as well as the use of a non-response weight adjustment and calibration strategy to mitigate the effects of attrition on the LISA estimates. The study focuses on data from waves 1 (2012) to 4 (2018) and uses practical examples based on selected demographic variables, to illustrate how attrition be assessed and treated.

Release date: 2022-11-14
8. Bayesian inference for a variance component model using pairwise composite likelihood with survey data
Articles and reports: 12-001-X202200100002
Description:
We consider an intercept only linear random effects model for analysis of data from a two stage cluster sampling design. At the first stage a simple random sample of clusters is drawn, and at the second stage a simple random sample of elementary units is taken within each selected cluster. The response variable is assumed to consist of a cluster-level random effect plus an independent error term with known variance. The objects of inference are the mean of the outcome variable and the random effect variance. With a more complex two stage sampling design, the use of an approach based on an estimated pairwise composite likelihood function has appealing properties. Our purpose is to use our simpler context to compare the results of likelihood inference with inference based on a pairwise composite likelihood function that is treated as an approximate likelihood, in particular treated as the likelihood component in Bayesian inference. In order to provide credible intervals having frequentist coverage close to nominal values, the pairwise composite likelihood function and corresponding posterior density need modification, such as a curvature adjustment. Through simulation studies, we investigate the performance of an adjustment proposed in the literature, and find that it works well for the mean but provides credible intervals for the random effect variance that suffer from under-coverage. We propose possible future directions including extensions to the case of a complex design.

Release date: 2022-06-21
9. Small area benchmarked estimation under the basic unit level model when the sampling rates are non-negligible
Articles and reports: 12-001-X202100100007
Description:
We consider the estimation of a small area mean under the basic unit-level model. The sum of the resulting model-dependent estimators may not add up to estimates obtained with a direct survey estimator that is deemed to be accurate for the union of these small areas. Benchmarking forces the model-based estimators to agree with the direct estimator at the aggregated area level. The generalized regression estimator is the direct estimator that we benchmark to. In this paper we compare small area benchmarked estimators based on four procedures. The first procedure produces benchmarked estimators by ratio adjustment. The second procedure is based on the empirical best linear unbiased estimator obtained under the unit-level model augmented with a suitable variable that ensures benchmarking. The third procedure uses pseudo-empirical estimators constructed with suitably chosen sampling weights so that, when aggregated, they agree with the reliable direct estimator for the larger area. The fourth procedure produces benchmarked estimators that are the result of a minimization problem subject to the constraint given by the benchmark condition. These benchmark procedures are applied to the small area estimators when the sampling rates are non-negligible. The resulting benchmarked estimators are compared in terms of relative bias and mean squared error using both a design-based simulation study as well as an example with real survey data.
Release date: 2021-06-24
10. Bayesian hierarchical weighting adjustment and survey inference
Articles and reports: 12-001-X202000200003
Description:
We combine weighting and Bayesian prediction in a unified approach to survey inference. The general principles of Bayesian analysis imply that models for survey outcomes should be conditional on all variables that affect the probability of inclusion. We incorporate all the variables that are used in the weighting adjustment under the framework of multilevel regression and poststratification, as a byproduct generating model-based weights after smoothing. We improve small area estimation by dealing with different complex issues caused by real-life applications to obtain robust inference at finer levels for subdomains of interest. We investigate deep interactions and introduce structured prior distributions for smoothing and stability of estimates. The computation is done via Stan and is implemented in the open-source R package rstanarm and available for public use. We evaluate the design-based properties of the Bayesian procedure. Simulation studies illustrate how the model-based prediction and weighting inference can outperform classical weighting. We apply the method to the New York Longitudinal Study of Wellbeing. The new approach generates smoothed weights and increases efficiency for robust finite population inference, especially for subsets of the population.
Release date: 2020-12-15

Reference (1)

Reference (1) ((1 result))

1. A donor imputation system to create a census database fully adjusted for underenumeration Archived
Surveys and statistical programs – Documentation: 11-522-X19990015668
Description:
Following the problems with estimating underenumeration in the 1991 Census of England and Wales the aim for the 2001 Census is to create a database that is fully adjusted to net underenumeration. To achieve this, the paper investigates weighted donor imputation methodology that utilises information from both the census and census coverage survey (CCS). The US Census Bureau has considered a similar approach for their 2000 Census (see Isaki et al 1998). The proposed procedure distinguishes between individuals who are not counted by the census because their household is missed and those who are missed in counted households. Census data is linked to data from the CCS. Multinomial logistic regression is used to estimate the probabilities that households are missed by the census and the probabilities that individuals are missed in counted households. Household and individual coverage weights are constructed from the estimated probabilities and these feed into the donor imputation procedure.
Release date: 2000-03-02

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Date modified:: 2024-04-18