Analysis
Filter results by
Search HelpKeyword(s)
Year of publication
Author(s)
Results
All (8)
All (8) ((8 results))
- Articles and reports: 12-001-X202400100014Description: This paper is an introduction to the special issue on the use of nonprobability samples featuring three papers that were presented at the 29th Morris Hansen Lecture by Courtney Kennedy, Yan Li and Jean-François Beaumont.Release date: 2024-06-25
- Articles and reports: 12-001-X201400114030Description:
The paper reports the results of a Monte Carlo simulation study that was conducted to compare the effectiveness of four different hierarchical Bayes small area models for producing state estimates of proportions based on data from stratified simple random samples from a fixed finite population. Two of the models adopted the commonly made assumptions that the survey weighted proportion for each sampled small area has a normal distribution and that the sampling variance of this proportion is known. One of these models used a linear linking model and the other used a logistic linking model. The other two models both employed logistic linking models and assumed that the sampling variance was unknown. One of these models assumed a normal distribution for the sampling model while the other assumed a beta distribution. The study found that for all four models the credible interval design-based coverage of the finite population state proportions deviated markedly from the 95 percent nominal level used in constructing the intervals.
Release date: 2014-06-27 - 3. On the definition and interpretation of interviewer variability for a complex sampling design ArchivedArticles and reports: 12-001-X200900110886Description:
Interviewer variability is a major component of variability of survey statistics. Different strategies related to question formatting, question phrasing, interviewer training, interviewer workload, interviewer experience and interviewer assignment are employed in an effort to reduce interviewer variability. The traditional formula for measuring interviewer variability, commonly referred to as the interviewer effect, is given by ieff := deff_int = 1 + (n bar sub int - 1) rho sub int, where rho sub int and n bar sub int are the intra-interviewer correlation and the simple average of the interviewer workloads, respectively. In this article, we provide a model-assisted justification of this well-known formula for equal probability of selection methods (epsem) with no spatial clustering in the sample and equal interviewer workload. However, spatial clustering and unequal weighting are both very common in large scale surveys. In the context of a complex sampling design, we obtain an appropriate formula for the interviewer variability that takes into consideration unequal probability of selection and spatial clustering. Our formula provides a more accurate assessment of interviewer effects and thus is helpful in allocating more reasonable amount of funds to control the interviewer variability. We also propose a decomposition of the overall effect into effects due to weighting, spatial clustering and interviewers. Such a decomposition is helpful in understanding ways to reduce total variance by different means.
Release date: 2009-06-22 - 4. Robust mean squared prediction error estimators of EBLUP of a small area total under the Fay-Herriot model ArchivedArticles and reports: 11-522-X200600110393Description:
In this paper we derive a second-order unbiased (or nearly unbiased) mean squared prediction error (MSPE) estimator of empirical best linear unbiased predictor (EBLUP) of a small area total for a non-normal extension to the well-known Fay-Herriot model. Specifically, we derive our MSPE estimator essentially assuming certain moment conditions on both the sampling and random effects distributions. The normality-based Prasad-Rao MSPE estimator has a surprising robustness property in that it remains second-order unbiased under the non-normality of random effects when a simple method-of-moments estimator is used for the variance component and the sampling error distribution is normal. We show that the normality-based MSPE estimator is no longer second-order unbiased when the sampling error distribution is non-normal or when the Fay-Herriot moment method is used to estimate the variance component, even when the sampling error distribution is normal. It is interesting to note that when the simple method-of moments estimator is used for the variance component, our proposed MSPE estimator does not require the estimation of kurtosis of the random effects. Results of a simulation study on the accuracy of the proposed MSPE estimator, under non-normality of both sampling and random effects distributions, are also presented.
Release date: 2008-03-17 - Articles and reports: 12-001-X20050018094Description:
Nested error regression models are frequently used in small-area estimation and related problems. Standard regression model selection criterion, when applied to nested error regression models, may result in inefficient model selection methods. We illustrate this point by examining the performance of the C_P statistic through a Monte Carlo simulation study. The inefficiency of the C_P statistic may, however, be rectified by a suitable transformation of the data.
Release date: 2005-07-21 - Articles and reports: 12-001-X19990014715Description:
The Gallup Organization has been conducting household surveys to study state-wide prevalences of alcohol and drug (e.g., cocaine, marijuana, etc.) use. Traditional design-based survey estimates of use and dependence for counties and select demographic groups have unacceptably large standard errors because sample sizes in sub-state groups are two small. Synthetic estimation incorporates demographic information and social indicators in estimates of prevalence through an implicit regression model. Synthetic estimates tend to have smaller variances than design-based estimates, but can be very homogeneous across counties when auxiliary variables are homogeneous. Composite estimates for small areas are weighted averages of design-based survey estimates and synthetic estimates. A second problem generally not encountered at the state level but present for sub-state areas and groups concerns estimating standard errors of estimated prevalences that are close to zero. This difficulty affects not only telephone household survey estimates, but also composite estimates. A hierarchical model is proposed to address this problem. Empirical Bayes composite estimators, which incorporate survey weights, of prevalences and jackknife estimators of their mean squared errors are presented and illustrated.
Release date: 1999-10-08 - 7. A model based justification of Kish's formula for design effects for weighting and clustering ArchivedArticles and reports: 12-001-X19990014718Description:
In this short note, we demonstrate that the well-known formula for the design effect intuitively proposed by Kish has a model-based justification. The formula can be interpreted as a conservative value for the actual design effect.
Release date: 1999-10-08 - Articles and reports: 12-001-X199200214480Description:
We consider the problem of estimating the “cost weights” and “relative importances” of different item strata for the local market basket areas. The estimation of these parameters is needed to construct the U.S. Consumer Price Index Numbers. We use multivariate models to construct composite estimators which combine information from relevant sources. The mean squared errors (MSE) of the proposed and the existing estimators are estimated using the repeated half samples available from the survey. Based on our numerical results, the proposed estimators seem to be superior to the existing estimators.
Release date: 1992-12-15
Stats in brief (0)
Stats in brief (0) (0 results)
No content available at this time.
Articles and reports (8)
Articles and reports (8) ((8 results))
- Articles and reports: 12-001-X202400100014Description: This paper is an introduction to the special issue on the use of nonprobability samples featuring three papers that were presented at the 29th Morris Hansen Lecture by Courtney Kennedy, Yan Li and Jean-François Beaumont.Release date: 2024-06-25
- Articles and reports: 12-001-X201400114030Description:
The paper reports the results of a Monte Carlo simulation study that was conducted to compare the effectiveness of four different hierarchical Bayes small area models for producing state estimates of proportions based on data from stratified simple random samples from a fixed finite population. Two of the models adopted the commonly made assumptions that the survey weighted proportion for each sampled small area has a normal distribution and that the sampling variance of this proportion is known. One of these models used a linear linking model and the other used a logistic linking model. The other two models both employed logistic linking models and assumed that the sampling variance was unknown. One of these models assumed a normal distribution for the sampling model while the other assumed a beta distribution. The study found that for all four models the credible interval design-based coverage of the finite population state proportions deviated markedly from the 95 percent nominal level used in constructing the intervals.
Release date: 2014-06-27 - 3. On the definition and interpretation of interviewer variability for a complex sampling design ArchivedArticles and reports: 12-001-X200900110886Description:
Interviewer variability is a major component of variability of survey statistics. Different strategies related to question formatting, question phrasing, interviewer training, interviewer workload, interviewer experience and interviewer assignment are employed in an effort to reduce interviewer variability. The traditional formula for measuring interviewer variability, commonly referred to as the interviewer effect, is given by ieff := deff_int = 1 + (n bar sub int - 1) rho sub int, where rho sub int and n bar sub int are the intra-interviewer correlation and the simple average of the interviewer workloads, respectively. In this article, we provide a model-assisted justification of this well-known formula for equal probability of selection methods (epsem) with no spatial clustering in the sample and equal interviewer workload. However, spatial clustering and unequal weighting are both very common in large scale surveys. In the context of a complex sampling design, we obtain an appropriate formula for the interviewer variability that takes into consideration unequal probability of selection and spatial clustering. Our formula provides a more accurate assessment of interviewer effects and thus is helpful in allocating more reasonable amount of funds to control the interviewer variability. We also propose a decomposition of the overall effect into effects due to weighting, spatial clustering and interviewers. Such a decomposition is helpful in understanding ways to reduce total variance by different means.
Release date: 2009-06-22 - 4. Robust mean squared prediction error estimators of EBLUP of a small area total under the Fay-Herriot model ArchivedArticles and reports: 11-522-X200600110393Description:
In this paper we derive a second-order unbiased (or nearly unbiased) mean squared prediction error (MSPE) estimator of empirical best linear unbiased predictor (EBLUP) of a small area total for a non-normal extension to the well-known Fay-Herriot model. Specifically, we derive our MSPE estimator essentially assuming certain moment conditions on both the sampling and random effects distributions. The normality-based Prasad-Rao MSPE estimator has a surprising robustness property in that it remains second-order unbiased under the non-normality of random effects when a simple method-of-moments estimator is used for the variance component and the sampling error distribution is normal. We show that the normality-based MSPE estimator is no longer second-order unbiased when the sampling error distribution is non-normal or when the Fay-Herriot moment method is used to estimate the variance component, even when the sampling error distribution is normal. It is interesting to note that when the simple method-of moments estimator is used for the variance component, our proposed MSPE estimator does not require the estimation of kurtosis of the random effects. Results of a simulation study on the accuracy of the proposed MSPE estimator, under non-normality of both sampling and random effects distributions, are also presented.
Release date: 2008-03-17 - Articles and reports: 12-001-X20050018094Description:
Nested error regression models are frequently used in small-area estimation and related problems. Standard regression model selection criterion, when applied to nested error regression models, may result in inefficient model selection methods. We illustrate this point by examining the performance of the C_P statistic through a Monte Carlo simulation study. The inefficiency of the C_P statistic may, however, be rectified by a suitable transformation of the data.
Release date: 2005-07-21 - Articles and reports: 12-001-X19990014715Description:
The Gallup Organization has been conducting household surveys to study state-wide prevalences of alcohol and drug (e.g., cocaine, marijuana, etc.) use. Traditional design-based survey estimates of use and dependence for counties and select demographic groups have unacceptably large standard errors because sample sizes in sub-state groups are two small. Synthetic estimation incorporates demographic information and social indicators in estimates of prevalence through an implicit regression model. Synthetic estimates tend to have smaller variances than design-based estimates, but can be very homogeneous across counties when auxiliary variables are homogeneous. Composite estimates for small areas are weighted averages of design-based survey estimates and synthetic estimates. A second problem generally not encountered at the state level but present for sub-state areas and groups concerns estimating standard errors of estimated prevalences that are close to zero. This difficulty affects not only telephone household survey estimates, but also composite estimates. A hierarchical model is proposed to address this problem. Empirical Bayes composite estimators, which incorporate survey weights, of prevalences and jackknife estimators of their mean squared errors are presented and illustrated.
Release date: 1999-10-08 - 7. A model based justification of Kish's formula for design effects for weighting and clustering ArchivedArticles and reports: 12-001-X19990014718Description:
In this short note, we demonstrate that the well-known formula for the design effect intuitively proposed by Kish has a model-based justification. The formula can be interpreted as a conservative value for the actual design effect.
Release date: 1999-10-08 - Articles and reports: 12-001-X199200214480Description:
We consider the problem of estimating the “cost weights” and “relative importances” of different item strata for the local market basket areas. The estimation of these parameters is needed to construct the U.S. Consumer Price Index Numbers. We use multivariate models to construct composite estimators which combine information from relevant sources. The mean squared errors (MSE) of the proposed and the existing estimators are estimated using the repeated half samples available from the survey. Based on our numerical results, the proposed estimators seem to be superior to the existing estimators.
Release date: 1992-12-15
Journals and periodicals (0)
Journals and periodicals (0) (0 results)
No content available at this time.
- Date modified: