Prueba para normalidad sesgada en el modelo lienal mixto con intercepto aleatorio
Skew normality test in a linear mixed model with random intercept
Keywords:
Modelos lineales mixtos, distribución normal sesgada, datos longitudinales (es)Linear mixed models, skew normal distribution, longitudinal data (en)
Downloads
Los modelos lineales mixtos se basan en el supuesto de que los efectos aleatorios y los errores son independientes y se distribuyen normalmente; sin embargo, este supuesto no siempre se satisface. Este trabajo propone una prueba para la detectar normalidad sesgada de los residuales y efectos aleatorios de un modelo lineal mixto. Para esto se presenta un método gráfico, usando simulación y se ilustra con unos datos reales. Se detectan casos lógicos donde los residuales del modelo lineal mixto se comportan acorde con las distribuciones Normal Sesgada y T Sesgada con la herramienta propuesta, proporcionando los análisis para mejorar la estimación.
Linear mixed models are based on the assumption that both the random eects and the errors are independent and normally distributed. In literature, analytical and graphical methods have been proposed to validate such assumption; nevertheless, this assumption is not always satised. This work proposes a test to identify skew normality in residuals. According to this a graphical method is presented, using simulation, and it is illustrated with real data. Logical cases are detected showing adjustment to skew- and t- distributions with the proposed test, enabling analysts to improve estimation.
References
Arellano, R. B.; Bolfarine, H.; Lachos, V. (2005), Skew-normal Linear Mixed Models. Journal of Data Science, 3, 415-438.
Azzalini, A. (1985), A class of distribution which includes the normal ones. Scand. J. Statist, 12, 171-178.
Azzalini, A.; Dalla Valle, A. (1996), The multivariate skew-normal distribution. Biometrika. 83(4), 715-726.
Azzalini, A.; Capitanio, A. (1999), Statistical applications of the multivariate skew normal distribution. J. Roy. Statist. Soc, series B, 6, 579-602.
Gurka M.; Edwards, Ll.; Muller, K.; Kupper, L. (2006), Extending the Box-Cox transformation to the linear mixed model. Journal of the Royal Statistical Society. Series A, 169(2), 273-288.
Lange, N.; Ryan, L. (1989), Assessing normality in random effects models. Ann. Statist, 17(2), 624-642.
Muñoz, H. (2004), Estudio In Vitro de los efectos e interacciones ambientales en el crecimiento y la producción de ácidos grasos poliinsaturados de las microalgas marinas. Tesis de Grado. Universidad Nacional de Colombia.
R Core Team (2014), R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org/.
Serfling, R. J. (1980), Approximation theorems of mathematical statistics. Wiley, New York. ISBN 0471024031
Valencia, M. (2010), Estimación en modelos lineales mixtos con datos continuos usando transformaciones y distribuciones no normales. Trabajo de Grado para optar al título de Magister en Ciencias-Estadística. Universidad Nacional de Colombia, Sede Medellín.
Verbeke, G.; Lesaffre, E. (1996), A linear mixed effects model with heterogeneity in the random effects population. J. Amer. Statist. Assoc., 91, 217-221.
Verbeke, G.; Molenberghs, G. (2001), Linear Mixed Models for Longitudinal Data. Springer, NY.
Zhou, T.; He, X. (2007), Three-step estimation in linear mixed models with skew-t distributions. Journal of Statistical Planning and Inference, 138, 1542-1555.
How to Cite
APA
ACM
ACS
ABNT
Chicago
Harvard
IEEE
MLA
Turabian
Vancouver
Download Citation
Article abstract page views
Downloads
License
The authors or copyright holders of each paper confer to the Journal of the Faculty of Sciences of Universidad Nacional de Colombia a non-exclusive, limited and free authorization on the paper that, once evaluated and approved, is sent for its subsequent publication in accordance with the following characteristics:
- The corrected version is sent according to the suggestions of the evaluators and it is clarified that the paper mentioned is an unpublished document on which the rights are authorized and full responsibility is assumed for the content of the work before both the Journal of the Faculty of Sciences, Universidad Nacional de Colombia and third parties.
- The authorization granted to the Journal will be in force from the date it is included in the respective volume and number of the Journal of the Faculty of Sciences in the Open Journal Systems and on the Journal’s home page (https://revistas.unal.edu.co/index.php/rfc/index), as well as in the different databases and data indexes in which the publication is indexed.
- The authors authorize the Journal of the Faculty of Sciences of Universidad Nacional de Colombia to publish the document in the format in which it is required (printed, digital, electronic or any other known or to be known) and authorize the Journal of the Faculty of Sciences to include the work in the indexes and search engines deemed necessary to promote its diffusion.
- The authors accept that the authorization is given free of charge, and therefore they waive any right to receive any emolument for the publication, distribution, public communication, and any other use made under the terms of this authorization.
- All the contents of the Journal of the Faculty of Sciences are published under the Creative Commons Attribution – Non-commercial – Without Derivative 4.0.License
MODEL LETTER OF PRESENTATION and TRANSFER OF COPYRIGHTS
Personal data processing policy
The names and email addresses entered in this Journal will be used exclusively for the purposes set out in it and will not be provided to third parties or used for other purposes.