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- Articles and reports: 12-001-X202000200003Description:
We combine weighting and Bayesian prediction in a unified approach to survey inference. The general principles of Bayesian analysis imply that models for survey outcomes should be conditional on all variables that affect the probability of inclusion. We incorporate all the variables that are used in the weighting adjustment under the framework of multilevel regression and poststratification, as a byproduct generating model-based weights after smoothing. We improve small area estimation by dealing with different complex issues caused by real-life applications to obtain robust inference at finer levels for subdomains of interest. We investigate deep interactions and introduce structured prior distributions for smoothing and stability of estimates. The computation is done via Stan and is implemented in the open-source R package rstanarm and available for public use. We evaluate the design-based properties of the Bayesian procedure. Simulation studies illustrate how the model-based prediction and weighting inference can outperform classical weighting. We apply the method to the New York Longitudinal Study of Wellbeing. The new approach generates smoothed weights and increases efficiency for robust finite population inference, especially for subsets of the population.
Release date: 2020-12-15 - Articles and reports: 12-001-X202000200004Description:
This article proposes a weight scaling method for Firth’s penalized likelihood for proportional hazards regression models. The method derives a relationship between the penalized likelihood that uses scaled weights and the penalized likelihood that uses unscaled weights, and it shows that the penalized likelihood that uses scaled weights have some desirable properties. A simulation study indicates that the penalized likelihood using scaled weights produces smaller biases in point estimates and standard errors than the biases produced by the penalized likelihood using unscaled weights. The weighted penalized likelihood is applied to estimate hazard rates for heart attacks by using a public-use data set from the National Health and Epidemiology Followup Study (NHEFS). SAS® statements to estimate hazard rates using data from complex surveys are given in the appendix.
Release date: 2020-12-15 - Articles and reports: 82-003-X202000700002Description:
This paper's objectives are to examine the feasibility of pooling linked population health surveys from three countries, facilitate the examination of health behaviours, and present useful information to assist in the planning of international population health surveillance and research studies.
Release date: 2020-07-29 - Articles and reports: 62F0014M2020008Description: This document describes the methodology and data source for the provincial monthly average retail prices table. This supplement also explains the difference between the Consumer Price Index and average retail prices in context of inflation.Release date: 2020-06-10
- Surveys and statistical programs – Documentation: 75F0002M2020001Description:
This note provides the definition of a first-time homebuyer concept used in the 2018 Canadian Housing Survey (CHS). It also includes the methodology used to identify first-time homebuyers and provides sensitivity analysis under alternative methodologies.
Release date: 2020-01-15
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Analysis (4)
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- Articles and reports: 12-001-X202000200003Description:
We combine weighting and Bayesian prediction in a unified approach to survey inference. The general principles of Bayesian analysis imply that models for survey outcomes should be conditional on all variables that affect the probability of inclusion. We incorporate all the variables that are used in the weighting adjustment under the framework of multilevel regression and poststratification, as a byproduct generating model-based weights after smoothing. We improve small area estimation by dealing with different complex issues caused by real-life applications to obtain robust inference at finer levels for subdomains of interest. We investigate deep interactions and introduce structured prior distributions for smoothing and stability of estimates. The computation is done via Stan and is implemented in the open-source R package rstanarm and available for public use. We evaluate the design-based properties of the Bayesian procedure. Simulation studies illustrate how the model-based prediction and weighting inference can outperform classical weighting. We apply the method to the New York Longitudinal Study of Wellbeing. The new approach generates smoothed weights and increases efficiency for robust finite population inference, especially for subsets of the population.
Release date: 2020-12-15 - Articles and reports: 12-001-X202000200004Description:
This article proposes a weight scaling method for Firth’s penalized likelihood for proportional hazards regression models. The method derives a relationship between the penalized likelihood that uses scaled weights and the penalized likelihood that uses unscaled weights, and it shows that the penalized likelihood that uses scaled weights have some desirable properties. A simulation study indicates that the penalized likelihood using scaled weights produces smaller biases in point estimates and standard errors than the biases produced by the penalized likelihood using unscaled weights. The weighted penalized likelihood is applied to estimate hazard rates for heart attacks by using a public-use data set from the National Health and Epidemiology Followup Study (NHEFS). SAS® statements to estimate hazard rates using data from complex surveys are given in the appendix.
Release date: 2020-12-15 - Articles and reports: 82-003-X202000700002Description:
This paper's objectives are to examine the feasibility of pooling linked population health surveys from three countries, facilitate the examination of health behaviours, and present useful information to assist in the planning of international population health surveillance and research studies.
Release date: 2020-07-29 - Articles and reports: 62F0014M2020008Description: This document describes the methodology and data source for the provincial monthly average retail prices table. This supplement also explains the difference between the Consumer Price Index and average retail prices in context of inflation.Release date: 2020-06-10
Reference (1)
Reference (1) ((1 result))
- Surveys and statistical programs – Documentation: 75F0002M2020001Description:
This note provides the definition of a first-time homebuyer concept used in the 2018 Canadian Housing Survey (CHS). It also includes the methodology used to identify first-time homebuyers and provides sensitivity analysis under alternative methodologies.
Release date: 2020-01-15