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A heteroscedastic measurement error model based on skew and heavy-tailed distributions with known error variances
Authors:Lorena Cáceres Tomaya
Affiliation:Instituto de Ciências Matemáticas e de Computa??o, Universidade de S?o Paulo, S?o Carlos, SP, Brasil
Abstract:In this paper, we study inference in a heteroscedastic measurement error model with known error variances. Instead of the normal distribution for the random components, we develop a model that assumes a skew-t distribution for the true covariate and a centred Student's t distribution for the error terms. The proposed model enables to accommodate skewness and heavy-tailedness in the data, while the degrees of freedom of the distributions can be different. Maximum likelihood estimates are computed via an EM-type algorithm. The behaviour of the estimators is also assessed in a simulation study. Finally, the approach is illustrated with a real data set from a methods comparison study in Analytical Chemistry.
Keywords:ECM algorithm  errors-in-variables model  heteroscedastic errors  maximum likelihood  skew-t distribution
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