| Popis: |
We consider the estimation of the insensitive parameter $\varepsilon$ in statistical models with $\varepsilon$ -insensitive loss functions. The properties of the maximum likelihood estimators are studied for the $\varepsilon$ -insensitive hyperbolic secant model. Focusing on the $\varepsilon$ -insensitive Laplace and Gauss models, we analyze the average generalization errors of maximum likelihood and Bayesian learning. It is shown that $\varepsilon$ -insensitive models behave as regular statistical models if the true generating distribution is in the interior of the parameter space, whereas non-regularity arises at the endpoint of the parameter space. |
