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- Articles and reports: 12-001-X202200100008Description:
The Multiple Imputation of Latent Classes (MILC) method combines multiple imputation and latent class analysis to correct for misclassification in combined datasets. Furthermore, MILC generates a multiply imputed dataset which can be used to estimate different statistics in a straightforward manner, ensuring that uncertainty due to misclassification is incorporated when estimating the total variance. In this paper, it is investigated how the MILC method can be adjusted to be applied for census purposes. More specifically, it is investigated how the MILC method deals with a finite and complete population register, how the MILC method can simultaneously correct misclassification in multiple latent variables and how multiple edit restrictions can be incorporated. A simulation study shows that the MILC method is in general able to reproduce cell frequencies in both low- and high-dimensional tables with low amounts of bias. In addition, variance can also be estimated appropriately, although variance is overestimated when cell frequencies are small.
Release date: 2022-06-21 - Articles and reports: 12-001-X202000100006Description:
In surveys, logical boundaries among variables or among waves of surveys make imputation of missing values complicated. We propose a new regression-based multiple imputation method to deal with survey nonresponses with two-sided logical boundaries. This imputation method automatically satisfies the boundary conditions without an additional acceptance/rejection procedure and utilizes the boundary information to derive an imputed value and to determine the suitability of the imputed value. Simulation results show that our new imputation method outperforms the existing imputation methods for both mean and quantile estimations regardless of missing rates, error distributions, and missing-mechanisms. We apply our method to impute the self-reported variable “years of smoking” in successive health screenings of Koreans.
Release date: 2020-06-30 - Articles and reports: 12-001-X201800254957Description:
When a linear imputation method is used to correct non-response based on certain assumptions, total variance can be assigned to non-responding units. Linear imputation is not as limited as it seems, given that the most common methods – ratio, donor, mean and auxiliary value imputation – are all linear imputation methods. We will discuss the inference framework and the unit-level decomposition of variance due to non-response. Simulation results will also be presented. This decomposition can be used to prioritize non-response follow-up or manual corrections, or simply to guide data analysis.
Release date: 2018-12-20 - Articles and reports: 11-633-X2017006Description:
This paper describes a method of imputing missing postal codes in a longitudinal database. The 1991 Canadian Census Health and Environment Cohort (CanCHEC), which contains information on individuals from the 1991 Census long-form questionnaire linked with T1 tax return files for the 1984-to-2011 period, is used to illustrate and validate the method. The cohort contains up to 28 consecutive fields for postal code of residence, but because of frequent gaps in postal code history, missing postal codes must be imputed. To validate the imputation method, two experiments were devised where 5% and 10% of all postal codes from a subset with full history were randomly removed and imputed.
Release date: 2017-03-13 - 5. Statistical matching using fractional imputation ArchivedArticles and reports: 12-001-X201600114539Description:
Statistical matching is a technique for integrating two or more data sets when information available for matching records for individual participants across data sets is incomplete. Statistical matching can be viewed as a missing data problem where a researcher wants to perform a joint analysis of variables that are never jointly observed. A conditional independence assumption is often used to create imputed data for statistical matching. We consider a general approach to statistical matching using parametric fractional imputation of Kim (2011) to create imputed data under the assumption that the specified model is fully identified. The proposed method does not have a convergent EM sequence if the model is not identified. We also present variance estimators appropriate for the imputation procedure. We explain how the method applies directly to the analysis of data from split questionnaire designs and measurement error models.
Release date: 2016-06-22 - Articles and reports: 12-001-X201500114193Description:
Imputed micro data often contain conflicting information. The situation may e.g., arise from partial imputation, where one part of the imputed record consists of the observed values of the original record and the other the imputed values. Edit-rules that involve variables from both parts of the record will often be violated. Or, inconsistency may be caused by adjustment for errors in the observed data, also referred to as imputation in Editing. Under the assumption that the remaining inconsistency is not due to systematic errors, we propose to make adjustments to the micro data such that all constraints are simultaneously satisfied and the adjustments are minimal according to a chosen distance metric. Different approaches to the distance metric are considered, as well as several extensions of the basic situation, including the treatment of categorical data, unit imputation and macro-level benchmarking. The properties and interpretations of the proposed methods are illustrated using business-economic data.
Release date: 2015-06-29 - Articles and reports: 12-001-X201400214089Description:
This manuscript describes the use of multiple imputation to combine information from multiple surveys of the same underlying population. We use a newly developed method to generate synthetic populations nonparametrically using a finite population Bayesian bootstrap that automatically accounts for complex sample designs. We then analyze each synthetic population with standard complete-data software for simple random samples and obtain valid inference by combining the point and variance estimates using extensions of existing combining rules for synthetic data. We illustrate the approach by combining data from the 2006 National Health Interview Survey (NHIS) and the 2006 Medical Expenditure Panel Survey (MEPS).
Release date: 2014-12-19 - 8. Fractional hot deck imputation for robust inference under item nonresponse in survey sampling ArchivedArticles and reports: 12-001-X201400214091Description:
Parametric fractional imputation (PFI), proposed by Kim (2011), is a tool for general purpose parameter estimation under missing data. We propose a fractional hot deck imputation (FHDI) which is more robust than PFI or multiple imputation. In the proposed method, the imputed values are chosen from the set of respondents and assigned proper fractional weights. The weights are then adjusted to meet certain calibration conditions, which makes the resulting FHDI estimator efficient. Two simulation studies are presented to compare the proposed method with existing methods.
Release date: 2014-12-19 - Articles and reports: 12-001-X201400114002Description:
We propose an approach for multiple imputation of items missing at random in large-scale surveys with exclusively categorical variables that have structural zeros. Our approach is to use mixtures of multinomial distributions as imputation engines, accounting for structural zeros by conceiving of the observed data as a truncated sample from a hypothetical population without structural zeros. This approach has several appealing features: imputations are generated from coherent, Bayesian joint models that automatically capture complex dependencies and readily scale to large numbers of variables. We outline a Gibbs sampling algorithm for implementing the approach, and we illustrate its potential with a repeated sampling study using public use census microdata from the state of New York, U.S.A.
Release date: 2014-06-27 - 10. Imputation for nonmonotone nonresponse in the survey of industrial research and development ArchivedArticles and reports: 12-001-X201200211753Description:
Nonresponse in longitudinal studies often occurs in a nonmonotone pattern. In the Survey of Industrial Research and Development (SIRD), it is reasonable to assume that the nonresponse mechanism is past-value-dependent in the sense that the response propensity of a study variable at time point t depends on response status and observed or missing values of the same variable at time points prior to t. Since this nonresponse is nonignorable, the parametric likelihood approach is sensitive to the specification of parametric models on both the joint distribution of variables at different time points and the nonresponse mechanism. The nonmonotone nonresponse also limits the application of inverse propensity weighting methods. By discarding all observed data from a subject after its first missing value, one can create a dataset with a monotone ignorable nonresponse and then apply established methods for ignorable nonresponse. However, discarding observed data is not desirable and it may result in inefficient estimators when many observed data are discarded. We propose to impute nonrespondents through regression under imputation models carefully created under the past-value-dependent nonresponse mechanism. This method does not require any parametric model on the joint distribution of the variables across time points or the nonresponse mechanism. Performance of the estimated means based on the proposed imputation method is investigated through some simulation studies and empirical analysis of the SIRD data.
Release date: 2012-12-19
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Analysis (56)
Analysis (56) (0 to 10 of 56 results)
- Articles and reports: 12-001-X202200100008Description:
The Multiple Imputation of Latent Classes (MILC) method combines multiple imputation and latent class analysis to correct for misclassification in combined datasets. Furthermore, MILC generates a multiply imputed dataset which can be used to estimate different statistics in a straightforward manner, ensuring that uncertainty due to misclassification is incorporated when estimating the total variance. In this paper, it is investigated how the MILC method can be adjusted to be applied for census purposes. More specifically, it is investigated how the MILC method deals with a finite and complete population register, how the MILC method can simultaneously correct misclassification in multiple latent variables and how multiple edit restrictions can be incorporated. A simulation study shows that the MILC method is in general able to reproduce cell frequencies in both low- and high-dimensional tables with low amounts of bias. In addition, variance can also be estimated appropriately, although variance is overestimated when cell frequencies are small.
Release date: 2022-06-21 - Articles and reports: 12-001-X202000100006Description:
In surveys, logical boundaries among variables or among waves of surveys make imputation of missing values complicated. We propose a new regression-based multiple imputation method to deal with survey nonresponses with two-sided logical boundaries. This imputation method automatically satisfies the boundary conditions without an additional acceptance/rejection procedure and utilizes the boundary information to derive an imputed value and to determine the suitability of the imputed value. Simulation results show that our new imputation method outperforms the existing imputation methods for both mean and quantile estimations regardless of missing rates, error distributions, and missing-mechanisms. We apply our method to impute the self-reported variable “years of smoking” in successive health screenings of Koreans.
Release date: 2020-06-30 - Articles and reports: 12-001-X201800254957Description:
When a linear imputation method is used to correct non-response based on certain assumptions, total variance can be assigned to non-responding units. Linear imputation is not as limited as it seems, given that the most common methods – ratio, donor, mean and auxiliary value imputation – are all linear imputation methods. We will discuss the inference framework and the unit-level decomposition of variance due to non-response. Simulation results will also be presented. This decomposition can be used to prioritize non-response follow-up or manual corrections, or simply to guide data analysis.
Release date: 2018-12-20 - Articles and reports: 11-633-X2017006Description:
This paper describes a method of imputing missing postal codes in a longitudinal database. The 1991 Canadian Census Health and Environment Cohort (CanCHEC), which contains information on individuals from the 1991 Census long-form questionnaire linked with T1 tax return files for the 1984-to-2011 period, is used to illustrate and validate the method. The cohort contains up to 28 consecutive fields for postal code of residence, but because of frequent gaps in postal code history, missing postal codes must be imputed. To validate the imputation method, two experiments were devised where 5% and 10% of all postal codes from a subset with full history were randomly removed and imputed.
Release date: 2017-03-13 - 5. Statistical matching using fractional imputation ArchivedArticles and reports: 12-001-X201600114539Description:
Statistical matching is a technique for integrating two or more data sets when information available for matching records for individual participants across data sets is incomplete. Statistical matching can be viewed as a missing data problem where a researcher wants to perform a joint analysis of variables that are never jointly observed. A conditional independence assumption is often used to create imputed data for statistical matching. We consider a general approach to statistical matching using parametric fractional imputation of Kim (2011) to create imputed data under the assumption that the specified model is fully identified. The proposed method does not have a convergent EM sequence if the model is not identified. We also present variance estimators appropriate for the imputation procedure. We explain how the method applies directly to the analysis of data from split questionnaire designs and measurement error models.
Release date: 2016-06-22 - Articles and reports: 12-001-X201500114193Description:
Imputed micro data often contain conflicting information. The situation may e.g., arise from partial imputation, where one part of the imputed record consists of the observed values of the original record and the other the imputed values. Edit-rules that involve variables from both parts of the record will often be violated. Or, inconsistency may be caused by adjustment for errors in the observed data, also referred to as imputation in Editing. Under the assumption that the remaining inconsistency is not due to systematic errors, we propose to make adjustments to the micro data such that all constraints are simultaneously satisfied and the adjustments are minimal according to a chosen distance metric. Different approaches to the distance metric are considered, as well as several extensions of the basic situation, including the treatment of categorical data, unit imputation and macro-level benchmarking. The properties and interpretations of the proposed methods are illustrated using business-economic data.
Release date: 2015-06-29 - Articles and reports: 12-001-X201400214089Description:
This manuscript describes the use of multiple imputation to combine information from multiple surveys of the same underlying population. We use a newly developed method to generate synthetic populations nonparametrically using a finite population Bayesian bootstrap that automatically accounts for complex sample designs. We then analyze each synthetic population with standard complete-data software for simple random samples and obtain valid inference by combining the point and variance estimates using extensions of existing combining rules for synthetic data. We illustrate the approach by combining data from the 2006 National Health Interview Survey (NHIS) and the 2006 Medical Expenditure Panel Survey (MEPS).
Release date: 2014-12-19 - 8. Fractional hot deck imputation for robust inference under item nonresponse in survey sampling ArchivedArticles and reports: 12-001-X201400214091Description:
Parametric fractional imputation (PFI), proposed by Kim (2011), is a tool for general purpose parameter estimation under missing data. We propose a fractional hot deck imputation (FHDI) which is more robust than PFI or multiple imputation. In the proposed method, the imputed values are chosen from the set of respondents and assigned proper fractional weights. The weights are then adjusted to meet certain calibration conditions, which makes the resulting FHDI estimator efficient. Two simulation studies are presented to compare the proposed method with existing methods.
Release date: 2014-12-19 - Articles and reports: 12-001-X201400114002Description:
We propose an approach for multiple imputation of items missing at random in large-scale surveys with exclusively categorical variables that have structural zeros. Our approach is to use mixtures of multinomial distributions as imputation engines, accounting for structural zeros by conceiving of the observed data as a truncated sample from a hypothetical population without structural zeros. This approach has several appealing features: imputations are generated from coherent, Bayesian joint models that automatically capture complex dependencies and readily scale to large numbers of variables. We outline a Gibbs sampling algorithm for implementing the approach, and we illustrate its potential with a repeated sampling study using public use census microdata from the state of New York, U.S.A.
Release date: 2014-06-27 - 10. Imputation for nonmonotone nonresponse in the survey of industrial research and development ArchivedArticles and reports: 12-001-X201200211753Description:
Nonresponse in longitudinal studies often occurs in a nonmonotone pattern. In the Survey of Industrial Research and Development (SIRD), it is reasonable to assume that the nonresponse mechanism is past-value-dependent in the sense that the response propensity of a study variable at time point t depends on response status and observed or missing values of the same variable at time points prior to t. Since this nonresponse is nonignorable, the parametric likelihood approach is sensitive to the specification of parametric models on both the joint distribution of variables at different time points and the nonresponse mechanism. The nonmonotone nonresponse also limits the application of inverse propensity weighting methods. By discarding all observed data from a subject after its first missing value, one can create a dataset with a monotone ignorable nonresponse and then apply established methods for ignorable nonresponse. However, discarding observed data is not desirable and it may result in inefficient estimators when many observed data are discarded. We propose to impute nonrespondents through regression under imputation models carefully created under the past-value-dependent nonresponse mechanism. This method does not require any parametric model on the joint distribution of the variables across time points or the nonresponse mechanism. Performance of the estimated means based on the proposed imputation method is investigated through some simulation studies and empirical analysis of the SIRD data.
Release date: 2012-12-19
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