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- Surveys and statistical programs – Documentation: 92-567-XDescription:
The Coverage Technical Report will present the error included in census data that results from persons missed by the 2006 Census or persons enumerated in error. Population coverage errors are one of the most important types of error because they affect not only the accuracy of population counts but also the accuracy of all of the census data describing characteristics of the population universe.
Release date: 2010-03-25 - Articles and reports: 12-001-X200900211042Description:
This paper proposes an approach for small area prediction based on data obtained from periodic surveys and censuses. We apply our approach to obtain population predictions for the municipalities not sampled in the Brazilian annual Household Survey (PNAD), as well as to increase the precision of the design-based estimates obtained for the sampled municipalities. In addition to the data provided by the PNAD, we use census demographic data from 1991 and 2000, as well as a complete population count conducted in 1996. Hierarchically non-structured and spatially structured growth models that gain strength from all the sampled municipalities are proposed and compared.
Release date: 2009-12-23 - Articles and reports: 11-522-X20040018750Description:
This paper modifies the link-tracing sampling with a sequential sample of sites and proposes a maximum likelihood estimator or another one derived under the Bayesian approach. It proposes that confidence intervals be constructed by Bootstrap methods.
Release date: 2005-10-27 - Articles and reports: 12-001-X20040027749Description:
A simple and practicable algorithm for constructing stratum boundaries in such a way that the coefficients of variation are equal in each stratum is derived for positively skewed populations. The new algorithm is shown to compare favourably with the cumulative root frequency method (Dalenius and Hodges 1957) and the Lavallée and Hidiroglou (1988) approximation method for estimating the optimum stratum boundaries.
Release date: 2005-02-03 - Articles and reports: 12-001-X20040027753Description:
Samplers often distrust model-based approaches to survey inference because of concerns about misspecification when models are applied to large samples from complex populations. We suggest that the model-based paradigm can work very successfully in survey settings, provided models are chosen that take into account the sample design and avoid strong parametric assumptions. The Horvitz-Thompson (HT) estimator is a simple design-unbiased estimator of the finite population total. From a modeling perspective, the HT estimator performs well when the ratios of the outcome values and the inclusion probabilities are exchangeable. When this assumption is not met, the HT estimator can be very inefficient. In Zheng and Little (2003, 2004) we used penalized splines (p-splines) to model smoothly - varying relationships between the outcome and the inclusion probabilities in one-stage probability proportional to size (PPS) samples. We showed that p spline model-based estimators are in general more efficient than the HT estimator, and can provide narrower confidence intervals with close to nominal confidence coverage. In this article, we extend this approach to two-stage sampling designs. We use a p-spline based mixed model that fits a nonparametric relationship between the primary sampling unit (PSU) means and a measure of PSU size, and incorporates random effects to model clustering. For variance estimation we consider the empirical Bayes model-based variance, the jackknife and balanced repeated replication (BRR) methods. Simulation studies on simulated data and samples drawn from public use microdata in the 1990 census demonstrate gains for the model-based p-spline estimator over the HT estimator and linear model-assisted estimators. Simulations also show the variance estimation methods yield confidence intervals with satisfactory confidence coverage. Interestingly, these gains can be seen for a common equal-probability design, where the first stage selection is PPS and the second stage selection probabilities are proportional to the inverse of the first stage inclusion probabilities, and the HT estimator leads to the unweighted mean. In situations that most favor the HT estimator, the model-based estimators have comparable efficiency.
Release date: 2005-02-03 - Articles and reports: 12-001-X20040027758Description:
In this article, we study the use of Bayesian neural networks in finite population estimation.We propose estimators for finite population mean and the associated mean squared error. We also propose to use the student t-distribution to model the disturbances in order to accommodate extreme observations that are often present in the data from social sample surveys. Numerical results show that Bayesian neural networks have made a significant improvement in finite population estimation over linear regression based methods
Release date: 2005-02-03 - 7. Sampling and Weighting (Reference Products: Technical Reports: 1996 Census of Population) ArchivedSurveys and statistical programs – Documentation: 92-371-XDescription:
This report deals with sampling and weighting, a process whereby certain characteristics are collected and processed for a random sample of dwellings and persons identified in the complete census enumeration. Data for the whole population are then obtained by scaling up the results for the sample to the full population level. The use of sampling may lead to substantial reductions in costs and respondent burden, or alternatively, can allow the scope of a census to be broadened at the same cost.
Release date: 1999-12-07 - Articles and reports: 12-001-X19980024355Description:
Two sampling strategies have been proposed for estimating the finite population total for the most recent occasion, based on the samples selected over two occasions involving varying probability sampling schemes. Attempts have been made to utilize the data collected on a study variable, in the first occasion, as a measure of size and a stratification variable for selection of the matched-sample on the second occasion. Relative efficiencies of the proposed strategies have been compared with suitable alternatives.
Release date: 1999-01-14
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- Articles and reports: 12-001-X200900211042Description:
This paper proposes an approach for small area prediction based on data obtained from periodic surveys and censuses. We apply our approach to obtain population predictions for the municipalities not sampled in the Brazilian annual Household Survey (PNAD), as well as to increase the precision of the design-based estimates obtained for the sampled municipalities. In addition to the data provided by the PNAD, we use census demographic data from 1991 and 2000, as well as a complete population count conducted in 1996. Hierarchically non-structured and spatially structured growth models that gain strength from all the sampled municipalities are proposed and compared.
Release date: 2009-12-23 - Articles and reports: 11-522-X20040018750Description:
This paper modifies the link-tracing sampling with a sequential sample of sites and proposes a maximum likelihood estimator or another one derived under the Bayesian approach. It proposes that confidence intervals be constructed by Bootstrap methods.
Release date: 2005-10-27 - Articles and reports: 12-001-X20040027749Description:
A simple and practicable algorithm for constructing stratum boundaries in such a way that the coefficients of variation are equal in each stratum is derived for positively skewed populations. The new algorithm is shown to compare favourably with the cumulative root frequency method (Dalenius and Hodges 1957) and the Lavallée and Hidiroglou (1988) approximation method for estimating the optimum stratum boundaries.
Release date: 2005-02-03 - Articles and reports: 12-001-X20040027753Description:
Samplers often distrust model-based approaches to survey inference because of concerns about misspecification when models are applied to large samples from complex populations. We suggest that the model-based paradigm can work very successfully in survey settings, provided models are chosen that take into account the sample design and avoid strong parametric assumptions. The Horvitz-Thompson (HT) estimator is a simple design-unbiased estimator of the finite population total. From a modeling perspective, the HT estimator performs well when the ratios of the outcome values and the inclusion probabilities are exchangeable. When this assumption is not met, the HT estimator can be very inefficient. In Zheng and Little (2003, 2004) we used penalized splines (p-splines) to model smoothly - varying relationships between the outcome and the inclusion probabilities in one-stage probability proportional to size (PPS) samples. We showed that p spline model-based estimators are in general more efficient than the HT estimator, and can provide narrower confidence intervals with close to nominal confidence coverage. In this article, we extend this approach to two-stage sampling designs. We use a p-spline based mixed model that fits a nonparametric relationship between the primary sampling unit (PSU) means and a measure of PSU size, and incorporates random effects to model clustering. For variance estimation we consider the empirical Bayes model-based variance, the jackknife and balanced repeated replication (BRR) methods. Simulation studies on simulated data and samples drawn from public use microdata in the 1990 census demonstrate gains for the model-based p-spline estimator over the HT estimator and linear model-assisted estimators. Simulations also show the variance estimation methods yield confidence intervals with satisfactory confidence coverage. Interestingly, these gains can be seen for a common equal-probability design, where the first stage selection is PPS and the second stage selection probabilities are proportional to the inverse of the first stage inclusion probabilities, and the HT estimator leads to the unweighted mean. In situations that most favor the HT estimator, the model-based estimators have comparable efficiency.
Release date: 2005-02-03 - Articles and reports: 12-001-X20040027758Description:
In this article, we study the use of Bayesian neural networks in finite population estimation.We propose estimators for finite population mean and the associated mean squared error. We also propose to use the student t-distribution to model the disturbances in order to accommodate extreme observations that are often present in the data from social sample surveys. Numerical results show that Bayesian neural networks have made a significant improvement in finite population estimation over linear regression based methods
Release date: 2005-02-03 - Articles and reports: 12-001-X19980024355Description:
Two sampling strategies have been proposed for estimating the finite population total for the most recent occasion, based on the samples selected over two occasions involving varying probability sampling schemes. Attempts have been made to utilize the data collected on a study variable, in the first occasion, as a measure of size and a stratification variable for selection of the matched-sample on the second occasion. Relative efficiencies of the proposed strategies have been compared with suitable alternatives.
Release date: 1999-01-14
Reference (2)
Reference (2) ((2 results))
- Surveys and statistical programs – Documentation: 92-567-XDescription:
The Coverage Technical Report will present the error included in census data that results from persons missed by the 2006 Census or persons enumerated in error. Population coverage errors are one of the most important types of error because they affect not only the accuracy of population counts but also the accuracy of all of the census data describing characteristics of the population universe.
Release date: 2010-03-25 - 2. Sampling and Weighting (Reference Products: Technical Reports: 1996 Census of Population) ArchivedSurveys and statistical programs – Documentation: 92-371-XDescription:
This report deals with sampling and weighting, a process whereby certain characteristics are collected and processed for a random sample of dwellings and persons identified in the complete census enumeration. Data for the whole population are then obtained by scaling up the results for the sample to the full population level. The use of sampling may lead to substantial reductions in costs and respondent burden, or alternatively, can allow the scope of a census to be broadened at the same cost.
Release date: 1999-12-07
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