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http://dx.doi.org/10.5351/KJAS.2015.28.2.361

Analysis of Field Test Data using Robust Linear Mixed-Effects Model  

Hong, Eun Hee (Department of Statistics, Seoul National University)
Lee, Youngjo (Department of Statistics, Seoul National University)
Ok, You Jin (Department of Statistics, Chonnam National University)
Na, Myung Hwan (Department of Statistics, Chonnam National University)
Noh, Maengseok (Department of Statistics, Pukyong National University)
Ha, Il Do (Department of Statistics, Pukyong National University)
Publication Information
The Korean Journal of Applied Statistics / v.28, no.2, 2015 , pp. 361-369 More about this Journal
Abstract
A general linear mixed-effects model is often used to analyze repeated measurement experiment data of a continuous response variable. However, a general linear mixed-effects model can give improper analysis results when simultaneously detecting heteroscedasticity and the non-normality of population distribution. To achieve a more robust estimation, we used a heavy-tailed linear mixed-effects model for a more exact and reliable analysis conclusion than a general linear mixed-effects model. We also provide reliability analysis results for further research.
Keywords
Heavy-tailed linear mixed-effects model; linear mixed-effects model; reliability analysis; repeated measurement experiment;
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  • Reference
1 Branco M. and Dey D. (2001). A general class of multivariate skew-elliptical distribution, Journal of Multivariate Analysis, 79, 93-113.
2 Ha, I. D., Noh, M. and Lee, Y. (2012). frailtyHL: A Package for fitting frailty models with h-likelihood, R Journal, 4, 28-37.
3 Lange, K. and Sinsheimer, J. S. (1993). Normal/independent distributions and their applications in robust regression, Journal of Computational and Graphical Statistics, 2, 175-198.
4 Lange, K., Little, J. A. and Taylor, M. G. J. (1989). Robust statistical modeling using the t distribution, Journal of American Statistical Association, 84, 881-896.
5 Lee, Y. and Nelder, J. A. (2006). Double hierarchical generalized linear models (with discussion), Applied Statistics, 55, 139-185.
6 Lee, Y., Nelder, J. A. and Pawitan, Y. (2006). Generalized Linear Models with Random Effects: Unified Analysis via H-likelihood, Chapman and Hall, London.
7 Lee, Y. and Noh, M. (2012). Modeling random effect variance with double hierarchical generalized linear models, Statistical Modelling, 12, 487-502.   DOI
8 Noh, M. and Lee, Y. (2007). Robust modeling for inference from GLM classes, Journal of American Statistical Association, 102, 1059-1072.   DOI   ScienceOn
9 Noh, M. and Lee, Y. (2011). dhglm: Double hierarchical generalized linear models. R package version 1.0, Available at http://CRAN.R-project.org/package=dhglm
10 Paik, M. C., Lee, Y. and Ha, I. D. (2015). Frequentist inference on random effects based on summarizability, Statistica Sinica, In press.
11 Self, S. G. and Liang, K. Y. (1987). Asymptotic properties of maximum likelihood estimators and likelihood ratio tests under nonstandard conditions, Journal of the American Statistical Association, 82, 605-610.   DOI   ScienceOn
12 Verbeke, G. and Molenberghs, G. (2003). Repeated Measures and Multilevel Modelling, Oxford, Eolss.
13 Wake eld, J. C., Smith, A. F. M., Racine-Poon, A. and Gelfand, A. E. (1994). Bayesian analysis of linear and nonlinear population models using the Gibbs sampler, Applied Statistics, 43, 201-222.   DOI   ScienceOn