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• 1. Small area quantile estimation via spline regression and empirical likelihood

  Articles and reports: 12-001-X201900100008

  Description:

  This paper studies small area quantile estimation under a unit level non-parametric nested-error regression model. We assume the small area specific error distributions satisfy a semi-parametric density ratio model. We fit the non-parametric model via the penalized spline regression method of Opsomer, Claeskens, Ranalli, Kauermann and Breidt (2008). Empirical likelihood is then applied to estimate the parameters in the density ratio model based on the residuals. This leads to natural area-specific estimates of error distributions. A kernel method is then applied to obtain smoothed error distribution estimates. These estimates are then used for quantile estimation in two situations: one is where we only have knowledge of covariate power means at the population level, the other is where we have covariate values of all sample units in the population. Simulation experiments indicate that the proposed methods for small area quantiles estimation work well for quantiles around the median in the first situation, and for a broad range of the quantiles in the second situation. A bootstrap mean square error estimator of the proposed estimators is also investigated. An empirical example based on Canadian income data is included.

  Release date: 2019-05-07

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• 2. Pseudo-likelihood-based Bayesian information criterion for variable selection in survey data Archived

  Articles and reports: 12-001-X201300211871

  Description:

  Regression models are routinely used in the analysis of survey data, where one common issue of interest is to identify influential factors that are associated with certain behavioral, social, or economic indices within a target population. When data are collected through complex surveys, the properties of classical variable selection approaches developed in i.i.d. non-survey settings need to be re-examined. In this paper, we derive a pseudo-likelihood-based BIC criterion for variable selection in the analysis of survey data and suggest a sample-based penalized likelihood approach for its implementation. The sampling weights are appropriately assigned to correct the biased selection result caused by the distortion between the sample and the target population. Under a joint randomization framework, we establish the consistency of the proposed selection procedure. The finite-sample performance of the approach is assessed through analysis and computer simulations based on data from the hypertension component of the 2009 Survey on Living with Chronic Diseases in Canada.

  Release date: 2014-01-15

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Articles and reports (2) ((2 results))

• 1. Small area quantile estimation via spline regression and empirical likelihood

  Articles and reports: 12-001-X201900100008

  Description:

  This paper studies small area quantile estimation under a unit level non-parametric nested-error regression model. We assume the small area specific error distributions satisfy a semi-parametric density ratio model. We fit the non-parametric model via the penalized spline regression method of Opsomer, Claeskens, Ranalli, Kauermann and Breidt (2008). Empirical likelihood is then applied to estimate the parameters in the density ratio model based on the residuals. This leads to natural area-specific estimates of error distributions. A kernel method is then applied to obtain smoothed error distribution estimates. These estimates are then used for quantile estimation in two situations: one is where we only have knowledge of covariate power means at the population level, the other is where we have covariate values of all sample units in the population. Simulation experiments indicate that the proposed methods for small area quantiles estimation work well for quantiles around the median in the first situation, and for a broad range of the quantiles in the second situation. A bootstrap mean square error estimator of the proposed estimators is also investigated. An empirical example based on Canadian income data is included.

  Release date: 2019-05-07

  ---

• 2. Pseudo-likelihood-based Bayesian information criterion for variable selection in survey data Archived

  Articles and reports: 12-001-X201300211871

  Description:

  Regression models are routinely used in the analysis of survey data, where one common issue of interest is to identify influential factors that are associated with certain behavioral, social, or economic indices within a target population. When data are collected through complex surveys, the properties of classical variable selection approaches developed in i.i.d. non-survey settings need to be re-examined. In this paper, we derive a pseudo-likelihood-based BIC criterion for variable selection in the analysis of survey data and suggest a sample-based penalized likelihood approach for its implementation. The sampling weights are appropriately assigned to correct the biased selection result caused by the distortion between the sample and the target population. Under a joint randomization framework, we establish the consistency of the proposed selection procedure. The finite-sample performance of the approach is assessed through analysis and computer simulations based on data from the hypertension component of the 2009 Survey on Living with Chronic Diseases in Canada.

  Release date: 2014-01-15

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