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  • 1. Small area estimation under transformation to linearity Archived
    Articles and reports: 12-001-X201100111446
    Description:

    Small area estimation based on linear mixed models can be inefficient when the underlying relationships are non-linear. In this paper we introduce SAE techniques for variables that can be modelled linearly following a non-linear transformation. In particular, we extend the model-based direct estimator of Chandra and Chambers (2005, 2009) to data that are consistent with a linear mixed model in the logarithmic scale, using model calibration to define appropriate weights for use in this estimator. Our results show that the resulting transformation-based estimator is both efficient and robust with respect to the distribution of the random effects in the model. An application to business survey data demonstrates the satisfactory performance of the method.

    Release date: 2011-06-29
  • 2. The use of estimating equations to perform a calibration on complex parameters Archived
    Articles and reports: 12-001-X201100111451
    Description:

    In the calibration method proposed by Deville and Särndal (1992), the calibration equations take only exact estimates of auxiliary variable totals into account. This article examines other parameters besides totals for calibration. Parameters that are considered complex include the ratio, median or variance of auxiliary variables.

    Release date: 2011-06-29
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  • 1. Small area estimation under transformation to linearity Archived
    Articles and reports: 12-001-X201100111446
    Description:

    Small area estimation based on linear mixed models can be inefficient when the underlying relationships are non-linear. In this paper we introduce SAE techniques for variables that can be modelled linearly following a non-linear transformation. In particular, we extend the model-based direct estimator of Chandra and Chambers (2005, 2009) to data that are consistent with a linear mixed model in the logarithmic scale, using model calibration to define appropriate weights for use in this estimator. Our results show that the resulting transformation-based estimator is both efficient and robust with respect to the distribution of the random effects in the model. An application to business survey data demonstrates the satisfactory performance of the method.

    Release date: 2011-06-29
  • 2. The use of estimating equations to perform a calibration on complex parameters Archived
    Articles and reports: 12-001-X201100111451
    Description:

    In the calibration method proposed by Deville and Särndal (1992), the calibration equations take only exact estimates of auxiliary variable totals into account. This article examines other parameters besides totals for calibration. Parameters that are considered complex include the ratio, median or variance of auxiliary variables.

    Release date: 2011-06-29
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