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- Articles and reports: 12-001-X201200211754Description:
The propensity-scoring-adjustment approach is commonly used to handle selection bias in survey sampling applications, including unit nonresponse and undercoverage. The propensity score is computed using auxiliary variables observed throughout the sample. We discuss some asymptotic properties of propensity-score-adjusted estimators and derive optimal estimators based on a regression model for the finite population. An optimal propensity-score-adjusted estimator can be implemented using an augmented propensity model. Variance estimation is discussed and the results from two simulation studies are presented.
Release date: 2012-12-19 - 2. Confidence interval estimation of small area parameters shrinking both means and variances ArchivedArticles and reports: 12-001-X201200211756Description:
We propose a new approach to small area estimation based on joint modelling of means and variances. The proposed model and methodology not only improve small area estimators but also yield "smoothed" estimators of the true sampling variances. Maximum likelihood estimation of model parameters is carried out using EM algorithm due to the non-standard form of the likelihood function. Confidence intervals of small area parameters are derived using a more general decision theory approach, unlike the traditional way based on minimizing the squared error loss. Numerical properties of the proposed method are investigated via simulation studies and compared with other competitive methods in the literature. Theoretical justification for the effective performance of the resulting estimators and confidence intervals is also provided.
Release date: 2012-12-19 - Articles and reports: 12-002-X201200111642Description:
It is generally recommended that weighted estimation approaches be used when analyzing data from a long-form census microdata file. Since such data files are now available in the RDC's, there is a need to provide researchers there with more information about doing weighted estimation with these files. The purpose of this paper is to provide some of this information - in particular, how the weight variables were derived for the census microdata files and what weight should be used for different units of analysis. For the 1996, 2001 and 2006 censuses the same weight variable is appropriate regardless of whether people, families or households are being studied. For the 1991 census, recommendations are more complex: a different weight variable is required for households than for people and families, and additional restrictions apply to obtain the correct weight value for families.
Release date: 2012-10-25 - Articles and reports: 12-001-X201200111684Description:
Many business surveys provide estimates for the monthly turnover for the major Standard Industrial Classification codes. This includes estimates for the change in the level of the monthly turnover compared to 12 months ago. Because business surveys often use overlapping samples, the turnover estimates in consecutive months are correlated. This makes the variance calculations for a change less straightforward. This article describes a general variance estimation procedure. The procedure allows for yearly stratum corrections when establishments move into other strata according to their actual sizes. The procedure also takes into account sample refreshments, births and deaths. The paper concludes with an example of the variance for the estimated yearly growth rate of the monthly turnover of Dutch Supermarkets.
Release date: 2012-06-27 - Articles and reports: 12-001-X201200111686Description:
We present a generalized estimating equations approach for estimating the concordance correlation coefficient and the kappa coefficient from sample survey data. The estimates and their accompanying standard error need to correctly account for the sampling design. Weighted measures of the concordance correlation coefficient and the kappa coefficient, along with the variance of these measures accounting for the sampling design, are presented. We use the Taylor series linearization method and the jackknife procedure for estimating the standard errors of the resulting parameter estimates. Body measurement and oral health data from the Third National Health and Nutrition Examination Survey are used to illustrate this methodology.
Release date: 2012-06-27
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- Articles and reports: 12-001-X201200211754Description:
The propensity-scoring-adjustment approach is commonly used to handle selection bias in survey sampling applications, including unit nonresponse and undercoverage. The propensity score is computed using auxiliary variables observed throughout the sample. We discuss some asymptotic properties of propensity-score-adjusted estimators and derive optimal estimators based on a regression model for the finite population. An optimal propensity-score-adjusted estimator can be implemented using an augmented propensity model. Variance estimation is discussed and the results from two simulation studies are presented.
Release date: 2012-12-19 - 2. Confidence interval estimation of small area parameters shrinking both means and variances ArchivedArticles and reports: 12-001-X201200211756Description:
We propose a new approach to small area estimation based on joint modelling of means and variances. The proposed model and methodology not only improve small area estimators but also yield "smoothed" estimators of the true sampling variances. Maximum likelihood estimation of model parameters is carried out using EM algorithm due to the non-standard form of the likelihood function. Confidence intervals of small area parameters are derived using a more general decision theory approach, unlike the traditional way based on minimizing the squared error loss. Numerical properties of the proposed method are investigated via simulation studies and compared with other competitive methods in the literature. Theoretical justification for the effective performance of the resulting estimators and confidence intervals is also provided.
Release date: 2012-12-19 - Articles and reports: 12-002-X201200111642Description:
It is generally recommended that weighted estimation approaches be used when analyzing data from a long-form census microdata file. Since such data files are now available in the RDC's, there is a need to provide researchers there with more information about doing weighted estimation with these files. The purpose of this paper is to provide some of this information - in particular, how the weight variables were derived for the census microdata files and what weight should be used for different units of analysis. For the 1996, 2001 and 2006 censuses the same weight variable is appropriate regardless of whether people, families or households are being studied. For the 1991 census, recommendations are more complex: a different weight variable is required for households than for people and families, and additional restrictions apply to obtain the correct weight value for families.
Release date: 2012-10-25 - Articles and reports: 12-001-X201200111684Description:
Many business surveys provide estimates for the monthly turnover for the major Standard Industrial Classification codes. This includes estimates for the change in the level of the monthly turnover compared to 12 months ago. Because business surveys often use overlapping samples, the turnover estimates in consecutive months are correlated. This makes the variance calculations for a change less straightforward. This article describes a general variance estimation procedure. The procedure allows for yearly stratum corrections when establishments move into other strata according to their actual sizes. The procedure also takes into account sample refreshments, births and deaths. The paper concludes with an example of the variance for the estimated yearly growth rate of the monthly turnover of Dutch Supermarkets.
Release date: 2012-06-27 - Articles and reports: 12-001-X201200111686Description:
We present a generalized estimating equations approach for estimating the concordance correlation coefficient and the kappa coefficient from sample survey data. The estimates and their accompanying standard error need to correctly account for the sampling design. Weighted measures of the concordance correlation coefficient and the kappa coefficient, along with the variance of these measures accounting for the sampling design, are presented. We use the Taylor series linearization method and the jackknife procedure for estimating the standard errors of the resulting parameter estimates. Body measurement and oral health data from the Third National Health and Nutrition Examination Survey are used to illustrate this methodology.
Release date: 2012-06-27
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