Skew normal measurement error models

Authors:
R. B. Arellano-Valle;S. Ozan;H. Bolfarine;V. H. Lachos
Affiliations:
Pontificia Universidad Catolica de Chile, Casilla 306, Correo 22, Santiago-Chile;Departamento de Estatistica-IME, Universidade de São Paulo, Caixa Postal 66281-CEP 05315 970, São Paulo, Brazil;Departamento de Estatistica-IME, Universidade de São Paulo, Caixa Postal 66281-CEP 05315 970, São Paulo, Brazil;Departamento de Estatistica-IME, Universidade de São Paulo, Caixa Postal 66281-CEP 05315 970, São Paulo, Brazil
Venue:
Journal of Multivariate Analysis
Year:
2005

Citing 2
Cited 2

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On fundamental skew distributions

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Comparison between a measurement error model and a linear model without measurement error

Computational Statistics & Data Analysis
Bayesian inference for dependent elliptical measurement error models

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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.