Abstract
In this paper, we study linear regression models in which the error term has shape mixtures of skew-normal distribution. This type of distribution belongs to the skew-normal (SN) distribution class that can be used for heavy tails and asymmetry data. For the first time, for the classical (non-Bayesian) estimation of the parameters of the SN family, we apply the Markov chains Monte Carlo ECM (MCMC-ECM) algorithm where the samples are generated by Gibbs sampling, denoted by Gibbs-ECM, and also, we extend two other types of the EM algorithm for the above model. Finally, the proposed method is evaluated through a simulation and compared with the Numerical Math-ECM algorithm and Monte Carlo ECM (MC-ECM) using a real data set.
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© 2024 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Frontmatter
- Likelihood and decoding problems for mixed space hidden Markov model
- A weight Monte Carlo estimation of fluctuations in branching processes
- Choice of a constant in the expression for the error of the Monte Carlo method
- Estimation in shape mixtures of skew-normal linear regression models via ECM coupled with Gibbs sampling
- An improved unbiased particle filter
- Partitions for stratified sampling
- A gradient method for high-dimensional BSDEs
- On bias reduction in parametric estimation in stage structured development models
Articles in the same Issue
- Frontmatter
- Likelihood and decoding problems for mixed space hidden Markov model
- A weight Monte Carlo estimation of fluctuations in branching processes
- Choice of a constant in the expression for the error of the Monte Carlo method
- Estimation in shape mixtures of skew-normal linear regression models via ECM coupled with Gibbs sampling
- An improved unbiased particle filter
- Partitions for stratified sampling
- A gradient method for high-dimensional BSDEs
- On bias reduction in parametric estimation in stage structured development models