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A Mixed Model-assisted Regression Estimator that Uses Variables Employed at the Design Stage

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Abstract

The Generalized regression estimator (GREG) of a finite population mean or total has been shown to be asymptotically optimal when the working linear regression model upon which it is based includes variables related to the sampling design. In this paper a regression estimator assisted by a linear mixed superpopulation model is proposed. It accounts for the extra information coming from the design in the random component of the model and saves degrees of freedom in finite sample estimation. This procedure combines the larger asymptotic efficiency of the optimal estimator and the greater finite sample stability of the GREG. Design based properties of the proposed estimator are discussed and a small simulation study is conducted to explore its finite sample performance.

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Authors and Affiliations

  1. Dipartimento di Economia, Finanza e Statistica, Università degli Studi di Perugia, Via A.Pascoli, 06123, Perugia, Italy

    Giorgio E. Montanari & M. Giovanna Ranalli

Authors
  1. Giorgio E. Montanari
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  2. M. Giovanna Ranalli
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Correspondence to Giorgio E. Montanari.

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Montanari, G.E., Ranalli, M.G. A Mixed Model-assisted Regression Estimator that Uses Variables Employed at the Design Stage. Stat. Meth. & Appl. 15, 139–149 (2006). https://doi.org/10.1007/s10260-006-0006-8

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  • DOI: https://doi.org/10.1007/s10260-006-0006-8

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