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  • 1. Variance estimation under composite imputation: The methodology behind SEVANI Archived
    Articles and reports: 12-001-X201100211605
    Description:

    Composite imputation is often used in business surveys. The term "composite" means that more than a single imputation method is used to impute missing values for a variable of interest. The literature on variance estimation in the presence of composite imputation is rather limited. To deal with this problem, we consider an extension of the methodology developed by Särndal (1992). Our extension is quite general and easy to implement provided that linear imputation methods are used to fill in the missing values. This class of imputation methods contains linear regression imputation, donor imputation and auxiliary value imputation, sometimes called cold-deck or substitution imputation. It thus covers the most common methods used by national statistical agencies for the imputation of missing values. Our methodology has been implemented in the System for the Estimation of Variance due to Nonresponse and Imputation (SEVANI) developed at Statistics Canada. Its performance is evaluated in a simulation study.

    Release date: 2011-12-21
  • 2. Maximum likelihood estimation for contingency tables and logistic regression with incorrectly linked data Archived
    Articles and reports: 12-001-X201100111444
    Description:

    Data linkage is the act of bringing together records that are believed to belong to the same unit (e.g., person or business) from two or more files. It is a very common way to enhance dimensions such as time and breadth or depth of detail. Data linkage is often not an error-free process and can lead to linking a pair of records that do not belong to the same unit. There is an explosion of record linkage applications, yet there has been little work on assuring the quality of analyses using such linked files. Naively treating such a linked file as if it were linked without errors will, in general, lead to biased estimates. This paper develops a maximum likelihood estimator for contingency tables and logistic regression with incorrectly linked records. The estimation technique is simple and is implemented using the well-known EM algorithm. A well known method of linking records in the present context is probabilistic data linking. The paper demonstrates the effectiveness of the proposed estimators in an empirical study which uses probabilistic data linkage.

    Release date: 2011-06-29
  • 3. The construction of stratified designs in R with the package stratification Archived
    Articles and reports: 12-001-X201100111447
    Description:

    This paper introduces a R-package for the stratification of a survey population using a univariate stratification variable X and for the calculation of stratum sample sizes. Non iterative methods such as the cumulative root frequency method and the geometric stratum boundaries are implemented. Optimal designs, with stratum boundaries that minimize either the CV of the simple expansion estimator for a fixed sample size n or the n value for a fixed CV can be constructed. Two iterative algorithms are available to find the optimal stratum boundaries. The design can feature a user defined certainty stratum where all the units are sampled. Take-all and take-none strata can be included in the stratified design as they might lead to smaller sample sizes. The sample size calculations are based on the anticipated moments of the survey variable Y, given the stratification variable X. The package handles conditional distributions of Y given X that are either a heteroscedastic linear model, or a log-linear model. Stratum specific non-response can be accounted for in the design construction and in the sample size calculations.

    Release date: 2011-06-29
  • 4. On the efficiency of randomized probability proportional to size sampling Archived
    Articles and reports: 12-001-X201100111450
    Description:

    This paper examines the efficiency of the Horvitz-Thompson estimator from a systematic probability proportional to size (PPS) sample drawn from a randomly ordered list. In particular, the efficiency is compared with that of an ordinary ratio estimator. The theoretical results are confirmed empirically with of a simulation study using Dutch data from the Producer Price Index.

    Release date: 2011-06-29
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  • 1. Variance estimation under composite imputation: The methodology behind SEVANI Archived
    Articles and reports: 12-001-X201100211605
    Description:

    Composite imputation is often used in business surveys. The term "composite" means that more than a single imputation method is used to impute missing values for a variable of interest. The literature on variance estimation in the presence of composite imputation is rather limited. To deal with this problem, we consider an extension of the methodology developed by Särndal (1992). Our extension is quite general and easy to implement provided that linear imputation methods are used to fill in the missing values. This class of imputation methods contains linear regression imputation, donor imputation and auxiliary value imputation, sometimes called cold-deck or substitution imputation. It thus covers the most common methods used by national statistical agencies for the imputation of missing values. Our methodology has been implemented in the System for the Estimation of Variance due to Nonresponse and Imputation (SEVANI) developed at Statistics Canada. Its performance is evaluated in a simulation study.

    Release date: 2011-12-21
  • 2. Maximum likelihood estimation for contingency tables and logistic regression with incorrectly linked data Archived
    Articles and reports: 12-001-X201100111444
    Description:

    Data linkage is the act of bringing together records that are believed to belong to the same unit (e.g., person or business) from two or more files. It is a very common way to enhance dimensions such as time and breadth or depth of detail. Data linkage is often not an error-free process and can lead to linking a pair of records that do not belong to the same unit. There is an explosion of record linkage applications, yet there has been little work on assuring the quality of analyses using such linked files. Naively treating such a linked file as if it were linked without errors will, in general, lead to biased estimates. This paper develops a maximum likelihood estimator for contingency tables and logistic regression with incorrectly linked records. The estimation technique is simple and is implemented using the well-known EM algorithm. A well known method of linking records in the present context is probabilistic data linking. The paper demonstrates the effectiveness of the proposed estimators in an empirical study which uses probabilistic data linkage.

    Release date: 2011-06-29
  • 3. The construction of stratified designs in R with the package stratification Archived
    Articles and reports: 12-001-X201100111447
    Description:

    This paper introduces a R-package for the stratification of a survey population using a univariate stratification variable X and for the calculation of stratum sample sizes. Non iterative methods such as the cumulative root frequency method and the geometric stratum boundaries are implemented. Optimal designs, with stratum boundaries that minimize either the CV of the simple expansion estimator for a fixed sample size n or the n value for a fixed CV can be constructed. Two iterative algorithms are available to find the optimal stratum boundaries. The design can feature a user defined certainty stratum where all the units are sampled. Take-all and take-none strata can be included in the stratified design as they might lead to smaller sample sizes. The sample size calculations are based on the anticipated moments of the survey variable Y, given the stratification variable X. The package handles conditional distributions of Y given X that are either a heteroscedastic linear model, or a log-linear model. Stratum specific non-response can be accounted for in the design construction and in the sample size calculations.

    Release date: 2011-06-29
  • 4. On the efficiency of randomized probability proportional to size sampling Archived
    Articles and reports: 12-001-X201100111450
    Description:

    This paper examines the efficiency of the Horvitz-Thompson estimator from a systematic probability proportional to size (PPS) sample drawn from a randomly ordered list. In particular, the efficiency is compared with that of an ordinary ratio estimator. The theoretical results are confirmed empirically with of a simulation study using Dutch data from the Producer Price Index.

    Release date: 2011-06-29
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