Record number :
Title of article :
Skew normal measurement error models
Author/Authors :
Arellano-Valle، نويسنده , , R.B. and Ozan، نويسنده , , S. and Bolfarine، نويسنده , , H. and Lachos، نويسنده , , V.H.، نويسنده ,
Issue Information :
دوفصلنامه با شماره پیاپی سال 2005
Pages :
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Abstract :
In this paper we define a class of skew normal measurement error models, extending usual symmetric normal models in order to avoid data transformation. The likelihood function of the observed data is obtained, which can be maximized by using existing statistical software. Inference on the parameters of interest can be approached by using the observed information matrix, which can also be computed by using existing statistical software, such as the Ox program. Bayesian inference is also discussed for the family of asymmetric models in terms of invariance with respect to the symmetric normal distribution showing that early results obtained for the normal distribution also holds for the asymmetric family. Results of a simulation study and an analysis of a real data set analysis are provided.
Keywords :
Invariance , Maximum likelihood , Posterior distribution , structural model , Prior distribution
Journal title :
Journal of Multivariate Analysis
Serial Year :
Link To Document :