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  • 1. Multiple model calibration Archived
    Articles and reports: 11-536-X200900110807
    Description:

    Model calibration (Wu & Sitter, JASA, 2001) has been shown to provide more efficient estimates than classical calibration when the values of one or more auxiliary variables are available for each unit in the population and the relationship between such variables and the variable of interest is more complex than a linear one. Model calibration, though, provides a different set of weights for each variable of interest. To overcome this problem an estimator is proposed: calibration is pursued with respect to both the auxiliary variables values and the fitted values of the variables of interest obtained with parametric and/or nonparametric models. This allows for coherence among estimates and more efficiency if the model is well specified. The asymptotic properties of the resulting estimator are studied with respect to the sampling design. The issue of high variability of the weights is addressed by relaxing binding constraints on the variables included for efficiency purposes in the calibration equations. A simulation study is also presented to better understand the finite size sample behavior of the proposed estimator

    Release date: 2009-08-11
  • 2. On regression estimation of finite population means Archived
    Articles and reports: 12-001-X19980013911
    Description:

    This paper examines the main properties of the generalized regression estimator of a finite population mean and those of the regression estimator obtained from the optimal difference estimator. Given that the latter can be more efficient than the former, conditions allowing this to happen are established, and a criterion for choosing between the two types of regression estimators follows. A simulation study illustrates their finite sample performances.

    Release date: 1998-07-31
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Articles and reports (2)

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  • 1. Multiple model calibration Archived
    Articles and reports: 11-536-X200900110807
    Description:

    Model calibration (Wu & Sitter, JASA, 2001) has been shown to provide more efficient estimates than classical calibration when the values of one or more auxiliary variables are available for each unit in the population and the relationship between such variables and the variable of interest is more complex than a linear one. Model calibration, though, provides a different set of weights for each variable of interest. To overcome this problem an estimator is proposed: calibration is pursued with respect to both the auxiliary variables values and the fitted values of the variables of interest obtained with parametric and/or nonparametric models. This allows for coherence among estimates and more efficiency if the model is well specified. The asymptotic properties of the resulting estimator are studied with respect to the sampling design. The issue of high variability of the weights is addressed by relaxing binding constraints on the variables included for efficiency purposes in the calibration equations. A simulation study is also presented to better understand the finite size sample behavior of the proposed estimator

    Release date: 2009-08-11
  • 2. On regression estimation of finite population means Archived
    Articles and reports: 12-001-X19980013911
    Description:

    This paper examines the main properties of the generalized regression estimator of a finite population mean and those of the regression estimator obtained from the optimal difference estimator. Given that the latter can be more efficient than the former, conditions allowing this to happen are established, and a criterion for choosing between the two types of regression estimators follows. A simulation study illustrates their finite sample performances.

    Release date: 1998-07-31
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