The traditional estimation of mixture regression models is based on the normal assumption of component errors and thus … is sensitive to outliers or heavy-tailed errors. A robust mixture regression model based on the t-distribution by … extending the mixture of t-distributions to the regression setting is proposed. However, this proposed new mixture regression …

the share of parents with altruistic preferences from those who are selfish, we estimate a finite mixture regression model …

estimating a finite mixture regression model for three different experimental data sets, two Swiss and one Chinese, over a large …

As mixture regression models increasingly receive attention from both theory and practice, the question of selecting … extensive simulation study that examines the performance of commonly used information criteria in a mixture regression context …

explanatory variables, mixture regression, Bayesian analy- sis, Markov Chain Monte Carlo ⁄We thank Dennis Fok, Philip Hans Franses … aggregated level. In case of one explanatory variable, the model simplifles to a mixture regression. A simple simulation … the aggregated information to estimate fl is to con- sider a mixture regression, see Quandt and Ramsey (1978); Everitt and …

As mixture regression models increasingly receive attention from both theory and practice, the question of selecting … extensive simulation study that examines the performance of commonly used information criteria in a mixture regression context …

As mixture regression models increasingly receive attention from both theory and practice, the question of selecting … extensive simulation study that examines the performance of commonly used information criteria in a mixture regression context …

estimator for a finite mixture regression model. We study the asymptotic properties such as consistency and the asymptotic …, and their importance for the statistical analysis of data is well documented. This article focuses on a finite mixture … regression model and develops an estimator of the parameters in the model using a minimum-distance technique. In general, minimum …