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- 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: 11-010-X20050088449Geography: Province or territoryDescription:
The purpose of this paper is to analyse geographic income disparities in Canada from the perspective of provinces and especially urban and rural areas. In particular, it looks at how per capita incomes vary across the urban-rural continuum - that is, how per capita incomes in large cities like Toronto and Montreal compare with medium sized cities like Halifax and Victoria, small cities like Brandon and Drummondville and with rural areas.
Release date: 2005-08-11 - 3. Provincial Income Disparities Through an Urban-rural Lens: Evidence from the 2001 Census ArchivedArticles and reports: 11-624-M2005012Geography: Province or territoryDescription:
This paper describes per capita employment income disparities across provinces and across the urban-rural continuum, from larger to small cities and between cities and rural areas. Its first objective is to compare the degree of income disparities across provinces to income disparities across the urban-rural continuum. Its second objective is to determine the extent to which provincial disparities can be tied to the urban-rural composition of provinces. The paper also seeks to determine whether urban-rural disparities in per capita employment income stem from poorer labour market conditions in smaller cities and rural areas compared to large cities.
Release date: 2005-07-21 - 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
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- 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: 11-010-X20050088449Geography: Province or territoryDescription:
The purpose of this paper is to analyse geographic income disparities in Canada from the perspective of provinces and especially urban and rural areas. In particular, it looks at how per capita incomes vary across the urban-rural continuum - that is, how per capita incomes in large cities like Toronto and Montreal compare with medium sized cities like Halifax and Victoria, small cities like Brandon and Drummondville and with rural areas.
Release date: 2005-08-11 - 3. Provincial Income Disparities Through an Urban-rural Lens: Evidence from the 2001 Census ArchivedArticles and reports: 11-624-M2005012Geography: Province or territoryDescription:
This paper describes per capita employment income disparities across provinces and across the urban-rural continuum, from larger to small cities and between cities and rural areas. Its first objective is to compare the degree of income disparities across provinces to income disparities across the urban-rural continuum. Its second objective is to determine the extent to which provincial disparities can be tied to the urban-rural composition of provinces. The paper also seeks to determine whether urban-rural disparities in per capita employment income stem from poorer labour market conditions in smaller cities and rural areas compared to large cities.
Release date: 2005-07-21 - 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
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