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A quasi-likelihood approach to the REML estimating equations

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  • Heyde, C. C.

Abstract

Difficult derivations of estimating functions via maximum likelihood methods can be often avoided and the results obtained under more general conditions using quasi-likelihood methods. In this note we illustrate the principle via the derivation of the restricted (or residual) (REML) estimating equations.

Suggested Citation

  • Heyde, C. C., 1994. "A quasi-likelihood approach to the REML estimating equations," Statistics & Probability Letters, Elsevier, vol. 21(5), pages 381-384, December.
    • Handle: RePEc:eee:stapro:v:21:y:1994:i:5:p:381-384
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    Cited by:

    1. Jiming Jiang & P. Lahiri, 2006. "Mixed model prediction and small area estimation," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 15(1), pages 1-96, June.
    2. Matt Higham & Michael Dumelle & Carly Hammond & Jay Hoef & Jeff Wells, 2024. "An Application of Spatio-Temporal Modeling to Finite Population Abundance Prediction," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 29(3), pages 491-515, September.
    3. Ping Wu & Li Xing Zhu, 2010. "An Orthogonality‐Based Estimation of Moments for Linear Mixed Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 37(2), pages 253-263, June.

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