El impacto de especificar incorrectamente la distribución de los efectos aleatorios en las estimaciones de modelos lineales generalizados mixtos

Diana María Arango Botero, Freddy Hernández Barajas

Resumen


La inferencia en modelos lineales generalizados mixtos está basada principalmente en la teoría de máxima verosimilitud, la cual asume que las estructuras tanto para la parte de los efectos fijos como de los efectos aleatorios están correctamente especificadas. Algunos autores han mostrado la sensibilidad de las estimaciones de los efectos fijos a especificaciones incorrectas de los efectos aleatorios. El objetivo de esta investigación es identificar, vía simulación, el impacto de la especificación incorrecta de la distribución de los efectos aleatorios en los modelos lineales generalizados mixtos, específicamente para los casos de las distribuciones Poisson y Binomial Negativa

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Referencias


Alonso, A., Litière, S. & Molenberghs, G. (2008), 'A family of tests to detect

misspecifications in the random-effects structure of generalized linear mixed

models', Computational statistics and data analysis 52(9), 4474_4486.

Alonso, A., Litière, S. & Molenberghs, G. (2010), 'Testing for misspecification in

generalized linear mixed models', Biostatistics 11(4), 771_786.

Alonso, A., Milanzi, E., Molenberghs, G., Buyck, C. & Bijnens, L. (2015), 'A

new modeling approach for quantifying expert opinion in the drug discovery

process', Statistics in medicine 34(9), 1590_1604.

Bolker, B. M., Brooks, M. E., Clark, C. J., Geange, S. W., Poulsen, J. R., Stevens,

M. H. H. & White, J. S. (2009), 'Generalized linear mixed models: a practical

guide for ecology and evolution', Trends in ecology and evolution 24(3), 127_

Cook, R. J., Lee, K. A. & Li, H. (2007), 'Non-inferiority trial design for recurrent

events', Statistics in medicine 26(25), 4563_4577.

DeGroot, M. H. & Schervish, M. J. (1988), Probabilidad y estadística, Editorial

Addison Wesley, Mexico.

Fabio, L. C., Paula, G. A. & De Castro, M. (2012), 'A Poisson mixed model with

nonnormal random effect distribution', Computational Statistics and Data

Analysis 56(6), 1499_1510.

Fitzmaurice, G. M., Laird, N. M. & Ware, J. H. (2011), Applied longitudinal analy-

sis, segunda edn, John Wiley and Sons, Boston, Massachusetts.

Gad, A. M. & El Kholy, R. B. (2012), 'Generalized Linear mixed models for Longitudinal

Data', International Journal of Probability and Statistics 1(3), 41_47.

Heagerty, P. J. & Kurland, B. F. (2001), 'Misspecified maximum likelihood estimates

and generalised linear mixed models', Biometrika 88(4), 973_985.

Hilbe, J. M. (2011), Negative binomial regression, Cambridge University Press.

Huang, X. (2009), 'Diagnosis of Random-Effect Model Misspecification in Generalized

Linear Mixed Models for Binary Response', Biometrics 65(2), 361_368.

Komárek, A. & Lesaffre, E. (2008), 'Generalized linear mixed model with a penalized

Gaussian mixture as a random effects distribution', Computational

Statistics and Data Analysis 52(7), 3441_3458.

Kondo, Y., Zhao, Y. & Petkau, J. (2015), 'A flexible mixed-effect negative binomial

regression model for detecting unusual increases in MRI lesion counts

in individual multiple sclerosis patients', Statistics in medicine 34(13), 2165_

Litière, S., Alonso, A. & Molenberghs, G. (2007), 'Type I and Type II Error

Under Random-Effects Misspecification in Generalized Linear Mixed Models',

Biometrics 63(4), 1038_1044.

Litière, S., Alonso, A. & Molenberghs, G. (2008), 'The impact of a misspecified

random-effects distribution on the estimation and the performance of inferential

procedures in generalized linear mixed models', Statistics in medicine

(16), 3125_3144.

McCulloch, C. E. & Neuhaus, J. M. (2011), 'Misspecifying the shape of a random

effects distribution: why getting it wrong may not matter', Statistical science

pp. 388_402.

Milanzi, E., Alonso, A. & Molenberghs, G. (2012), 'Ignoring overdispersion in

hierarchical loglinear models: Possible problems and solutions', Statistics in

medicine 31(14), 1475_1482.

Molenberghs, G. & Verbeke, G. (2005), Models for Discrete Longitudinal Data.

Springer Series in Statistics, Springer.

Neuhaus, J. M., Hauck, W. W. & Kalbfleisch, J. D. (1992), 'The effects of mixture

distribution misspecification when fitting mixed-effects logistic models',

Biometrika 79(4), 755_762.

Neuhaus, J. M. & McCulloch, C. E. (2006), 'Separating between-and within-cluster

covariate effects by using conditional and partitioning methods', Journal of

the Royal Statistical Society: Series B (Statistical Methodology) 68(5), 859_

Neuhaus, J. M. & McCulloch, C. E. (2011a), 'Estimation of covariate effects in

generalized linear mixed models with informative cluster sizes', Biometrika

(1), 147_162.

Neuhaus, J. M. & McCulloch, C. E. (2011b), 'The effect of misspecification of random

e_ects distributions in clustered data settings with outcome-dependent

sampling', Canadian Journal of Statistics 39(3), 488_497.

Neuhaus, J. M., McCulloch, C. E. & Boylan, R. (2011), 'A Note on Type II Error

Under Random Effects Misspecification in Generalized Linear Mixed Models',

Biometrics 67(2), 654_656.

Neuhaus, J. M., McCulloch, C. E. & Boylan, R. (2012), 'Estimation of covariate

effects in generalized linear mixed models with a misspecified distribution of

random intercepts and slopes', Statistics in medicine 32(14), 2419_2429.

Spiessens, B., Lesaffre, E., Verbeke, G. & Kim, K. (2002), 'Group Sequential Methods

for an Ordinal Logistic Random-Effects Model Under Misspecification',

Biometrics 58(3), 569_575.

Tsonaka, R., Rizopoulos, D., Verbeke, G. & Lesa_re, E. (2010), 'Nonignorable models

for intermittently missing categorical longitudinal responses', Biometrics

(3), 834_844.

Valencia, A. (2014), 'El uso de la distribución gh en riesgo operativo', Contaduría

y administración 59(1), 123_148.

Verbeke, G. & Lesaffre, E. (1997), 'The effect of misspecifying the random-effects

distribution in linear mixed models for longitudinal data', Computational Statistics and Data Analysis 23(4), 541_556.

Verbeke, G. & Molenberghs, G. (2013), 'The gradient function as an exploratory

goodness-of-_t assessment of the random-effects distribution in mixed models',

Biostatistics 14(3), 477.

Xiang, L., Yau, K. K. & Lee, A. H. (2012), 'The robust estimation method for a

_nite mixture of Poisson mixed-effect models', Computational Statistics and

Data Analysis 56(6), 1994_2005.

Zhao, Y., Li, D. K., Petkau, A. J., Riddehough, A. & Traboulsee, A. (2014), 'Detection

of unusual increases in MRI lesion counts in individual multiple sclerosis

patients', Journal of the American Statistical Association 109(505), 119_132.


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ISSN: 2027-3355 – ISSN Online: 2339-3076