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Some Estimation Methods and Their Assessment in Multilevel Models: A Review

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  • Yahia S El-Horbaty

    (Department of Mathematics, Insurance and Applied Statistics, Helwan University, Egypt)

  • Eman M Hanafy

    (Department of Mathematics, Insurance and Applied Statistics, Helwan University, Egypt)

Abstract

Multilevel linear regression models represent a generalization of linear models in which the regression coefficients are themselves given a model whose parameters are also estimated from the data. This paper reviews multilevel random coefficients regression models with a focus on the estimation problem and its assessment. Parameter estimation for the fixed effects and the variance components are highlighted. In addition, comparisons that are made in the literature to choose among the competing methods are highlighted. This is particularly emphasized when some of the assumptions underlying the estimation methods are violated.

Suggested Citation

  • Yahia S El-Horbaty & Eman M Hanafy, 2018. "Some Estimation Methods and Their Assessment in Multilevel Models: A Review," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 5(3), pages 69-76, February.
    • Handle: RePEc:adp:jbboaj:v:5:y:2018:i:3:p:69-76
    DOI: 10.19080/BBOAJ.2018.05.555662
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    References listed on IDEAS

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    3. Kloke, John D. & McKean, Joseph W. & Rashid, M. Mushfiqur, 2009. "Rank-Based Estimation and Associated Inferences for Linear Models With Cluster Correlated Errors," Journal of the American Statistical Association, American Statistical Association, vol. 104(485), pages 384-390.
    4. White, Halbert, 1982. "Maximum Likelihood Estimation of Misspecified Models," Econometrica, Econometric Society, vol. 50(1), pages 1-25, January.
    5. Harvey Goldstein & Roderick McDonald, 1988. "A general model for the analysis of multilevel data," Psychometrika, Springer;The Psychometric Society, vol. 53(4), pages 455-467, December.
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