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  • 1. Small area estimation of survey weighted counts under aggregated level spatial model Archived
    Articles and reports: 12-001-X201900100006
    Description:

    The empirical predictor under an area level version of the generalized linear mixed model (GLMM) is extensively used in small area estimation (SAE) for counts. However, this approach does not use the sampling weights or clustering information that are essential for valid inference given the informative samples produced by modern complex survey designs. This paper describes an SAE method that incorporates this sampling information when estimating small area proportions or counts under an area level version of the GLMM. The approach is further extended under a spatial dependent version of the GLMM (SGLMM). The mean squared error (MSE) estimation for this method is also discussed. This SAE method is then applied to estimate the extent of household poverty in different districts of the rural part of the state of Uttar Pradesh in India by linking data from the 2011-12 Household Consumer Expenditure Survey collected by the National Sample Survey Office (NSSO) of India, and the 2011 Indian Population Census. Results from this application indicate a substantial gain in precision for the new methods compared to the direct survey estimates.

    Release date: 2019-05-07
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  • 1. Small area estimation of survey weighted counts under aggregated level spatial model Archived
    Articles and reports: 12-001-X201900100006
    Description:

    The empirical predictor under an area level version of the generalized linear mixed model (GLMM) is extensively used in small area estimation (SAE) for counts. However, this approach does not use the sampling weights or clustering information that are essential for valid inference given the informative samples produced by modern complex survey designs. This paper describes an SAE method that incorporates this sampling information when estimating small area proportions or counts under an area level version of the GLMM. The approach is further extended under a spatial dependent version of the GLMM (SGLMM). The mean squared error (MSE) estimation for this method is also discussed. This SAE method is then applied to estimate the extent of household poverty in different districts of the rural part of the state of Uttar Pradesh in India by linking data from the 2011-12 Household Consumer Expenditure Survey collected by the National Sample Survey Office (NSSO) of India, and the 2011 Indian Population Census. Results from this application indicate a substantial gain in precision for the new methods compared to the direct survey estimates.

    Release date: 2019-05-07
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