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  • 1. Bayes linear estimation for finite population with emphasis on categorical data Archived
    Articles and reports: 12-001-X201400111886
    Description:

    Bayes linear estimator for finite population is obtained from a two-stage regression model, specified only by the means and variances of some model parameters associated with each stage of the hierarchy. Many common design-based estimators found in the literature can be obtained as particular cases. A new ratio estimator is also proposed for the practical situation in which auxiliary information is available. The same Bayes linear approach is proposed for obtaining estimation of proportions for multiple categorical data associated with finite population units, which is the main contribution of this work. A numerical example is provided to illustrate it.

    Release date: 2014-06-27
  • 2. Estimation and replicate variance estimation of deciles for complex survey data from positively skewed populations Archived
    Articles and reports: 12-001-X201300211868
    Description:

    Thompson and Sigman (2000) introduced an estimation procedure for estimating medians from highly positively skewed population data. Their procedure uses interpolation over data-dependent intervals (bins). The earlier paper demonstrated that this procedure has good statistical properties for medians computed from a highly skewed sample. This research extends the previous work to decile estimation methods for a positively skewed population using complex survey data. We present three different interpolation methods along with the traditional decile estimation method (no bins) and evaluate each method empirically, using residential housing data from the Survey of Construction and via a simulation study. We found that a variant of the current procedure using the 95th percentile as a scaling factor produces decile estimates with the best statistical properties.

    Release date: 2014-01-15
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  • 1. Bayes linear estimation for finite population with emphasis on categorical data Archived
    Articles and reports: 12-001-X201400111886
    Description:

    Bayes linear estimator for finite population is obtained from a two-stage regression model, specified only by the means and variances of some model parameters associated with each stage of the hierarchy. Many common design-based estimators found in the literature can be obtained as particular cases. A new ratio estimator is also proposed for the practical situation in which auxiliary information is available. The same Bayes linear approach is proposed for obtaining estimation of proportions for multiple categorical data associated with finite population units, which is the main contribution of this work. A numerical example is provided to illustrate it.

    Release date: 2014-06-27
  • 2. Estimation and replicate variance estimation of deciles for complex survey data from positively skewed populations Archived
    Articles and reports: 12-001-X201300211868
    Description:

    Thompson and Sigman (2000) introduced an estimation procedure for estimating medians from highly positively skewed population data. Their procedure uses interpolation over data-dependent intervals (bins). The earlier paper demonstrated that this procedure has good statistical properties for medians computed from a highly skewed sample. This research extends the previous work to decile estimation methods for a positively skewed population using complex survey data. We present three different interpolation methods along with the traditional decile estimation method (no bins) and evaluate each method empirically, using residential housing data from the Survey of Construction and via a simulation study. We found that a variant of the current procedure using the 95th percentile as a scaling factor produces decile estimates with the best statistical properties.

    Release date: 2014-01-15
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