Robust factorial ANCOVA with LTS error distributions

Robust Learning Algorithm with LTS Error Function

Feedforward neural networks (FFNs) are often considered as universal tools and find their applications in areas such as function approximation, pattern recognition, or signal and image processing. One of the main advantages of using FFNs is that they usually do not require, in the learning process, exact mathematical knowledge about input-output dependencies. In other words, they may be regarde...

Robust LTS rules with the Combilex speech technology lexicon

Combilex is a high quality pronunciation lexicon, aimed at speech technology applications, that has recently been released by CSTR. Combilex benefits from several advanced features. This paper evaluates one of these: the explicit alignment of phones to graphemes in a word. This alignment can help to rapidly develop robust and accurate letter-to-sound (LTS) rules, without needing to rely on auto...

Fast and robust bootstrap for LTS

The Least Trimmed Squares (LTS) estimator is a frequently used robust estimator of regression. When it comes to inference for the parameters of the regression model, the asymptotic normality of the LTS estimator can be used. However, this is usually not appropriate in situations where the use of robust estimators is recommended. The bootstrap method constitutes an alternative, but has two major...

Robust model selection criteria for robust S and LTS estimators

Outliers and multi-collinearity often have large influence in the model/variable selection process in linear regression analysis. To investigate this combined problem of multi-collinearity and outliers, we studied and compared Liu-type S (liuS-estimators) and Liu-type Least Trimmed Squares (liuLTS) estimators as robust model selection criteria. Therefore, the main goal this study is to select s...

Model-Robust Factorial Designs