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All (3) ((3 results))

  • 1. Bagging non-differentiable estimators in complex surveys Archived
    Articles and reports: 12-001-X201400214118
    Description:

    Bagging is a powerful computational method used to improve the performance of inefficient estimators. This article is a first exploration of the use of bagging in survey estimation, and we investigate the effects of bagging on non-differentiable survey estimators including sample distribution functions and quantiles, among others. The theoretical properties of bagged survey estimators are investigated under both design-based and model-based regimes. In particular, we show the design consistency of the bagged estimators, and obtain the asymptotic normality of the estimators in the model-based context. The article describes how implementation of bagging for survey estimators can take advantage of replicates developed for survey variance estimation, providing an easy way for practitioners to apply bagging in existing surveys. A major remaining challenge in implementing bagging in the survey context is variance estimation for the bagged estimators themselves, and we explore two possible variance estimation approaches. Simulation experiments reveal the improvement of the proposed bagging estimator relative to the original estimator and compare the two variance estimation approaches.

    Release date: 2014-12-19
  • 2. Geozones: An area-based method for analysis of health outcomes Archived
    Articles and reports: 82-003-X201200111633
    Geography: Canada
    Description:

    This paper explains the methodology for creating Geozones, which are area-based thresholds of population characteristics derived from census data, which can be used in the analysis of social or economic differences in health and health service utilization.

    Release date: 2012-03-21
  • 3. Data sparseness in contextual population health research: effects of small group size and cluster analysis on linear and non-linear multilevel models Archived
    Articles and reports: 11-522-X200600110418
    Description:

    The current use of multilevel models to examine the effects of surrounding contexts on health outcomes attest to their value as a statistical method for analyzing grouped data. But the use of multilevel modeling with data from population-based surveys is often limited by the small number of cases per level-2 unit, prompting a recent trend in the neighborhood literature to apply cluster analysis techniques to address the problem of data sparseness. In this paper we use Monte Carlo simulations to investigate the effects of marginal group sizes and cluster analysis techniques on the validity of parameter estimates in both linear and non-linear multilevel models.

    Release date: 2008-03-17
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Analysis (3)

Analysis (3) ((3 results))

  • 1. Bagging non-differentiable estimators in complex surveys Archived
    Articles and reports: 12-001-X201400214118
    Description:

    Bagging is a powerful computational method used to improve the performance of inefficient estimators. This article is a first exploration of the use of bagging in survey estimation, and we investigate the effects of bagging on non-differentiable survey estimators including sample distribution functions and quantiles, among others. The theoretical properties of bagged survey estimators are investigated under both design-based and model-based regimes. In particular, we show the design consistency of the bagged estimators, and obtain the asymptotic normality of the estimators in the model-based context. The article describes how implementation of bagging for survey estimators can take advantage of replicates developed for survey variance estimation, providing an easy way for practitioners to apply bagging in existing surveys. A major remaining challenge in implementing bagging in the survey context is variance estimation for the bagged estimators themselves, and we explore two possible variance estimation approaches. Simulation experiments reveal the improvement of the proposed bagging estimator relative to the original estimator and compare the two variance estimation approaches.

    Release date: 2014-12-19
  • 2. Geozones: An area-based method for analysis of health outcomes Archived
    Articles and reports: 82-003-X201200111633
    Geography: Canada
    Description:

    This paper explains the methodology for creating Geozones, which are area-based thresholds of population characteristics derived from census data, which can be used in the analysis of social or economic differences in health and health service utilization.

    Release date: 2012-03-21
  • 3. Data sparseness in contextual population health research: effects of small group size and cluster analysis on linear and non-linear multilevel models Archived
    Articles and reports: 11-522-X200600110418
    Description:

    The current use of multilevel models to examine the effects of surrounding contexts on health outcomes attest to their value as a statistical method for analyzing grouped data. But the use of multilevel modeling with data from population-based surveys is often limited by the small number of cases per level-2 unit, prompting a recent trend in the neighborhood literature to apply cluster analysis techniques to address the problem of data sparseness. In this paper we use Monte Carlo simulations to investigate the effects of marginal group sizes and cluster analysis techniques on the validity of parameter estimates in both linear and non-linear multilevel models.

    Release date: 2008-03-17
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