Recursive residuals for linear mixed models
Ahmed Bani-Mustafa (),
K. M. Matawie,
C. F. Finch,
Amjad Al-Nasser and
Enrico Ciavolino
Additional contact information
Ahmed Bani-Mustafa: Australian College of Kuwait
K. M. Matawie: Western Sydney University
C. F. Finch: Edith Cowan University
Quality & Quantity: International Journal of Methodology, 2019, vol. 53, issue 3, No 9, 1263-1274
Abstract:
Abstract This paper presents and extends the concept of recursive residuals and their estimation to an important class of statistical models, Linear Mixed Models (LMM). Recurrence formulae are developed and recursive residuals are defined. Recursive computable expressions are also developed for the model’s likelihood, together with its derivative and information matrix. The theoretical framework for developing recursive residuals and their estimation for LMM varies with the estimation method used, such as the fitting-of-constants or the Best Linear Unbiased Predictor method. These methods are illustrated through application to an LMM example drawn from a published study. Model fit is assessed through a graphical display of the developed recursive residuals and their Cumulative Sums.
Keywords: BLUP; Fitting-of-constant; Linear mixed model; Recursive estimation; Recursive residuals (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (3)
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Persistent link: https://EconPapers.repec.org/RePEc:spr:qualqt:v:53:y:2019:i:3:d:10.1007_s11135-018-0814-6
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DOI: 10.1007/s11135-018-0814-6
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