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  • Surveys and statistical programs – Documentation: 12-001-X201200211758
    Description:

    This paper develops two Bayesian methods for inference about finite population quantiles of continuous survey variables from unequal probability sampling. The first method estimates cumulative distribution functions of the continuous survey variable by fitting a number of probit penalized spline regression models on the inclusion probabilities. The finite population quantiles are then obtained by inverting the estimated distribution function. This method is quite computationally demanding. The second method predicts non-sampled values by assuming a smoothly-varying relationship between the continuous survey variable and the probability of inclusion, by modeling both the mean function and the variance function using splines. The two Bayesian spline-model-based estimators yield a desirable balance between robustness and efficiency. Simulation studies show that both methods yield smaller root mean squared errors than the sample-weighted estimator and the ratio and difference estimators described by Rao, Kovar, and Mantel (RKM 1990), and are more robust to model misspecification than the regression through the origin model-based estimator described in Chambers and Dunstan (1986). When the sample size is small, the 95% credible intervals of the two new methods have closer to nominal confidence coverage than the sample-weighted estimator.

    Release date: 2012-12-19

  • Articles and reports: 98-311-X2011001
    Geography: Province or territory, Census metropolitan area
    Description:

    This 2011 Census analytical document presents key trends emerging from the analysis of age and sex data in Canada, provinces and territories, census metropolitan areas (CMAs), census agglomerations (CAs), regions located outside CMAs and CAs as well as municipalities.

    Release date: 2012-05-29

  • Table: 98-311-X2011002
    Description:

    These data tables present 2011 Census highlights on age and sex. They display distribution by broad age groups.

    Available on the official day of release, they present information highlights via key indicators such as percentage change and percent distribution, for various levels of geography. The tables also allow users to perform simple rank and sort functions.

    Release date: 2012-05-29
Data (1)

Data (1) ((1 result))

  • Table: 98-311-X2011002
    Description:

    These data tables present 2011 Census highlights on age and sex. They display distribution by broad age groups.

    Available on the official day of release, they present information highlights via key indicators such as percentage change and percent distribution, for various levels of geography. The tables also allow users to perform simple rank and sort functions.

    Release date: 2012-05-29
Analysis (1)

Analysis (1) ((1 result))

  • Articles and reports: 98-311-X2011001
    Geography: Province or territory, Census metropolitan area
    Description:

    This 2011 Census analytical document presents key trends emerging from the analysis of age and sex data in Canada, provinces and territories, census metropolitan areas (CMAs), census agglomerations (CAs), regions located outside CMAs and CAs as well as municipalities.

    Release date: 2012-05-29
Reference (1)

Reference (1) ((1 result))

  • Surveys and statistical programs – Documentation: 12-001-X201200211758
    Description:

    This paper develops two Bayesian methods for inference about finite population quantiles of continuous survey variables from unequal probability sampling. The first method estimates cumulative distribution functions of the continuous survey variable by fitting a number of probit penalized spline regression models on the inclusion probabilities. The finite population quantiles are then obtained by inverting the estimated distribution function. This method is quite computationally demanding. The second method predicts non-sampled values by assuming a smoothly-varying relationship between the continuous survey variable and the probability of inclusion, by modeling both the mean function and the variance function using splines. The two Bayesian spline-model-based estimators yield a desirable balance between robustness and efficiency. Simulation studies show that both methods yield smaller root mean squared errors than the sample-weighted estimator and the ratio and difference estimators described by Rao, Kovar, and Mantel (RKM 1990), and are more robust to model misspecification than the regression through the origin model-based estimator described in Chambers and Dunstan (1986). When the sample size is small, the 95% credible intervals of the two new methods have closer to nominal confidence coverage than the sample-weighted estimator.

    Release date: 2012-12-19
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