%0 Journal Article %1 lachos_robust_2009 %A Lachos, V. H. %A Dey, D. K. %A Cancho, V. G. %D 2009 %J Journal of Statistical Planning and Inference %K bias, contaminated distribution distribution, effects, normal, outliers, random skew, slash %N 12 %P 4098--4110 %R 10.1016/j.jspi.2009.05.040, %T Robust linear mixed models with skew-normal independent distributions from a Bayesian perspective %U http://www.sciencedirect.com/science/article/pii/S0378375809001669 %V 139 %X Linear mixed models were developed to handle clustered data and have been a topic of increasing interest in statistics for the past 50 years. Generally, the normality (or symmetry) of the random effects is a common assumption in linear mixed models but it may, sometimes, be unrealistic, obscuring important features of among-subjects variation. In this article, we utilize skew-normal/independent distributions as a tool for robust modeling of linear mixed models under a Bayesian paradigm. The skew-normal/independent distributions is an attractive class of asymmetric heavy-tailed distributions that includes the skew-normal distribution, skew-t, skew-slash and the skew-contaminated normal distributions as special cases, providing an appealing robust alternative to the routine use of symmetric distributions in this type of models. The methods developed are illustrated using a real data set from Framingham cholesterol study. Keywords Gibbs algorithms; Linear mixed models; MCMC; Metropolis–Hastings; Skew-normal/independent distribution

@article{lachos_robust_2009, abstract = {Linear mixed models were developed to handle clustered data and have been a topic of increasing interest in statistics for the past 50 years. Generally, the normality (or symmetry) of the random effects is a common assumption in linear mixed models but it may, sometimes, be unrealistic, obscuring important features of among-subjects variation. In this article, we utilize skew-normal/independent distributions as a tool for robust modeling of linear mixed models under a Bayesian paradigm. The skew-normal/independent distributions is an attractive class of asymmetric heavy-tailed distributions that includes the skew-normal distribution, skew-t, skew-slash and the skew-contaminated normal distributions as special cases, providing an appealing robust alternative to the routine use of symmetric distributions in this type of models. The methods developed are illustrated using a real data set from Framingham cholesterol study. Keywords Gibbs algorithms; Linear mixed models; MCMC; Metropolis–Hastings; Skew-normal/independent distribution}, added-at = {2017-01-09T13:57:26.000+0100}, author = {Lachos, V. H. and Dey, D. K. and Cancho, V. G.}, biburl = {https://www.bibsonomy.org/bibtex/2640b1658935da7b1ccac0b6b56b3ecf8/yourwelcome}, doi = {10.1016/j.jspi.2009.05.040,}, interhash = {305761b68a19cea2bafc3068ee9f7166}, intrahash = {640b1658935da7b1ccac0b6b56b3ecf8}, journal = {Journal of Statistical Planning and Inference}, keywords = {bias, contaminated distribution distribution, effects, normal, outliers, random skew, slash}, number = 12, pages = {4098--4110}, timestamp = {2017-01-09T14:01:11.000+0100}, title = {Robust linear mixed models with skew-normal independent distributions from a {Bayesian} perspective}, url = {http://www.sciencedirect.com/science/article/pii/S0378375809001669}, urldate = {2012-06-23}, volume = 139, year = 2009 }