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Small Area Estimation Under a Mixture Model

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  • U C Sud
  • Hukum Chandra
  • HVL Bathla

Abstract

Small area estimation (SAE) under a linear mixed model may not be efficient if data contain substantial proportion of zeros than would be expected under standard model assumptions (hereafter zero-inflated data). We discuss the SAE for zero-inflated data under a mixture model (Fletcher et al., 2005 and Karlberg, 2000) that account for excess zeros in the data. Our results from simulation studies show that mixture model based approach for SAE works well and produces an efficient set of small area estimates. An application to real survey data from the National Sample Survey Organisation of India demonstrates the satisfactory performance of the approach.

Suggested Citation

  • U C Sud & Hukum Chandra & HVL Bathla, 2010. "Small Area Estimation Under a Mixture Model," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 11(3), pages 503-516, December.
    • Handle: RePEc:csb:stintr:v:11:y:2010:i:3:p:503-516
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    References listed on IDEAS

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    1. Gonzalez-Manteiga, W. & Lombardia, M.J. & Molina, I. & Morales, D. & Santamaria, L., 2007. "Estimation of the mean squared error of predictors of small area linear parameters under a logistic mixed model," Computational Statistics & Data Analysis, Elsevier, vol. 51(5), pages 2720-2733, February.
    2. Jiang, Jiming & Lahiri, P., 2006. "Estimation of Finite Population Domain Means: A Model-Assisted Empirical Best Prediction Approach," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 301-311, March.
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