Weighting and estimation
Results
All (4)
All (4) ((4 results))
- 1. Comparison of survey regression techniques in the context of small area estimation of poverty ArchivedArticles and reports: 12-001-X201000211378Description:
One key to poverty alleviation or eradication in the third world is reliable information on the poor and their location, so that interventions and assistance can be effectively targeted to the neediest people. Small area estimation is one statistical technique that is used to monitor poverty and to decide on aid allocation in pursuit of the Millennium Development Goals. Elbers, Lanjouw and Lanjouw (ELL) (2003) proposed a small area estimation methodology for income-based or expenditure-based poverty measures, which is implemented by the World Bank in its poverty mapping projects via the involvement of the central statistical agencies in many third world countries, including Cambodia, Lao PDR, the Philippines, Thailand and Vietnam, and is incorporated into the World Bank software program PovMap. In this paper, the ELL methodology which consists of first modeling survey data and then applying that model to census information is presented and discussed with strong emphasis on the first phase, i.e., the fitting of regression models and on the estimated standard errors at the second phase. Other regression model fitting procedures such as the General Survey Regression (GSR) (as described in Lohr (1999) Chapter 11) and those used in existing small area estimation techniques: Pseudo-Empirical Best Linear Unbiased Prediction (Pseudo-EBLUP) approach (You and Rao 2002) and Iterative Weighted Estimating Equation (IWEE) method (You, Rao and Kovacevic 2003) are presented and compared with the ELL modeling strategy. The most significant difference between the ELL method and the other techniques is in the theoretical underpinning of the ELL model fitting procedure. An example based on the Philippines Family Income and Expenditure Survey is presented to show the differences in both the parameter estimates and their corresponding standard errors, and in the variance components generated from the different methods and the discussion is extended to the effect of these on the estimated accuracy of the final small area estimates themselves. The need for sound estimation of variance components, as well as regression estimates and estimates of their standard errors for small area estimation of poverty is emphasized.
Release date: 2010-12-21 - Articles and reports: 12-001-X20040016992Description:
In the U.S. Census of Population and Housing, a sample of about one-in-six of the households receives a longer version of the census questionnaire called the long form. All others receive a version called the short form. Raking, using selected control totals from the short form, has been used to create two sets of weights for long form estimation; one for individuals and one for households. We describe a weight construction method based on quadratic programming that produces household weights such that the weighted sum for individual characteristics and for household characteristics agree closely with selected short form totals. The method is broadly applicable to situations where weights are to be constructed to meet both size bounds and sum-to-control restrictions. Application to the situation where the controls are estimates with an estimated covariance matrix is described.
Release date: 2004-07-14 - Articles and reports: 12-001-X20030026777Description:
The Accuracy and Coverage Evaluation survey was conducted to estimate the coverage in the 2000 U.S. Census. After field procedures were completed, several types of missing data had to be addressed to apply dual-system estimation. Some housing units were not interviewed. Two noninterview adjustments were devised from the same set of interviews, one for each of two points in time. In addition, the resident, match, or enumeration status of some respondents was not determined. Methods applied in the past were replaced to accommodate a tighter schedule to compute and verify the estimates. This paper presents the extent of missing data in the survey, describes the procedures applied, comparing them to past and current alternatives, and provides analytical summaries of the procedures, including comparisons of dual-system estimates of population under alternatives. Because the resulting levels of missing data were low, it appears that alternative procedures would not have affected the results substantially. However some changes in the estimates are noted.
Release date: 2004-01-27 - 4. Estimation of census adjustment factors ArchivedArticles and reports: 12-001-X20000015176Description:
A components-of-variance approach and an estimated covariance error structure were used in constructing predictors of adjustment factors for the 1990 Decennial Census. The variability of the estimated covariance matrix is the suspected cause of certain anomalies that appeared in the regression estimation and in the estimated adjustment factors. We investigate alternative prediction methods and propose a procedure that is less influenced by variability in the estimated covariance matrix. The proposed methodology is applied to a data set composed of 336 adjustment factors from the 1990 Post Enumeration Survey.
Release date: 2000-08-30
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Analysis (4)
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- 1. Comparison of survey regression techniques in the context of small area estimation of poverty ArchivedArticles and reports: 12-001-X201000211378Description:
One key to poverty alleviation or eradication in the third world is reliable information on the poor and their location, so that interventions and assistance can be effectively targeted to the neediest people. Small area estimation is one statistical technique that is used to monitor poverty and to decide on aid allocation in pursuit of the Millennium Development Goals. Elbers, Lanjouw and Lanjouw (ELL) (2003) proposed a small area estimation methodology for income-based or expenditure-based poverty measures, which is implemented by the World Bank in its poverty mapping projects via the involvement of the central statistical agencies in many third world countries, including Cambodia, Lao PDR, the Philippines, Thailand and Vietnam, and is incorporated into the World Bank software program PovMap. In this paper, the ELL methodology which consists of first modeling survey data and then applying that model to census information is presented and discussed with strong emphasis on the first phase, i.e., the fitting of regression models and on the estimated standard errors at the second phase. Other regression model fitting procedures such as the General Survey Regression (GSR) (as described in Lohr (1999) Chapter 11) and those used in existing small area estimation techniques: Pseudo-Empirical Best Linear Unbiased Prediction (Pseudo-EBLUP) approach (You and Rao 2002) and Iterative Weighted Estimating Equation (IWEE) method (You, Rao and Kovacevic 2003) are presented and compared with the ELL modeling strategy. The most significant difference between the ELL method and the other techniques is in the theoretical underpinning of the ELL model fitting procedure. An example based on the Philippines Family Income and Expenditure Survey is presented to show the differences in both the parameter estimates and their corresponding standard errors, and in the variance components generated from the different methods and the discussion is extended to the effect of these on the estimated accuracy of the final small area estimates themselves. The need for sound estimation of variance components, as well as regression estimates and estimates of their standard errors for small area estimation of poverty is emphasized.
Release date: 2010-12-21 - Articles and reports: 12-001-X20040016992Description:
In the U.S. Census of Population and Housing, a sample of about one-in-six of the households receives a longer version of the census questionnaire called the long form. All others receive a version called the short form. Raking, using selected control totals from the short form, has been used to create two sets of weights for long form estimation; one for individuals and one for households. We describe a weight construction method based on quadratic programming that produces household weights such that the weighted sum for individual characteristics and for household characteristics agree closely with selected short form totals. The method is broadly applicable to situations where weights are to be constructed to meet both size bounds and sum-to-control restrictions. Application to the situation where the controls are estimates with an estimated covariance matrix is described.
Release date: 2004-07-14 - Articles and reports: 12-001-X20030026777Description:
The Accuracy and Coverage Evaluation survey was conducted to estimate the coverage in the 2000 U.S. Census. After field procedures were completed, several types of missing data had to be addressed to apply dual-system estimation. Some housing units were not interviewed. Two noninterview adjustments were devised from the same set of interviews, one for each of two points in time. In addition, the resident, match, or enumeration status of some respondents was not determined. Methods applied in the past were replaced to accommodate a tighter schedule to compute and verify the estimates. This paper presents the extent of missing data in the survey, describes the procedures applied, comparing them to past and current alternatives, and provides analytical summaries of the procedures, including comparisons of dual-system estimates of population under alternatives. Because the resulting levels of missing data were low, it appears that alternative procedures would not have affected the results substantially. However some changes in the estimates are noted.
Release date: 2004-01-27 - 4. Estimation of census adjustment factors ArchivedArticles and reports: 12-001-X20000015176Description:
A components-of-variance approach and an estimated covariance error structure were used in constructing predictors of adjustment factors for the 1990 Decennial Census. The variability of the estimated covariance matrix is the suspected cause of certain anomalies that appeared in the regression estimation and in the estimated adjustment factors. We investigate alternative prediction methods and propose a procedure that is less influenced by variability in the estimated covariance matrix. The proposed methodology is applied to a data set composed of 336 adjustment factors from the 1990 Post Enumeration Survey.
Release date: 2000-08-30
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