Skew normal measurement error models |
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Authors: | RB Arellano-Valle S Ozan H Bolfarine VH Lachos |
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Institution: | aPontificia Universidad Catolica de Chile, Casilla 306, Correo 22, Santiago-Chile;bDepartamento de Estatistica-IME, Universidade de São Paulo, Caixa Postal 66281-CEP 05315 970, São Paulo, Brazil |
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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. |
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Keywords: | Invariance Maximum likelihood Posterior distribution Prior distribution Structural model |
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