In this paper we consider two novel kernel machine based feature extraction algorithms in a regression settings. The first method is derived based on the principles underlying the recently introduced Maximum Margin Discimination Analysis (MMDA) algorithm. However, here it is shown that the orthogonalization principle employed by the original MMDA algorithm can be motivated using the well-known ...

Artificial neural networks provide powerful models for solving many economic classifications, as well as regression problems. For example, they were successfully used for the discrimination between healthy economic agents and those prone to bankruptcy, for the inflation-deflation forecasting, for the currency exchange rates prediction, or for the prediction of share prices. At present, the neur...

We improve upon the Carlin and Chib MCMC algorithm that searches in model and parameter space. Our proposed algorithm attempts non-uniformly chosen ‘local’ moves in model space and avoids some pitfalls of other existing algorithms. In a series of examples with linear and logistic regression we report evidence that our proposed algorithm performs better than existing algorithms.

In the context of genomic selection in animal breeding, an important objective consists in looking for explicative markers for a phenotype under study. In order to deal with a high number of markers, we propose to use combinatorial optimization to perform variable selection. Results show that our approach outperforms some classical and widely used methods on simulated and “closed to real” datas...

In the presence of covariate measurement error, estimating a regression function nonparametrically is extremely difŽ cult, the problem being related to deconvolution. Various frequentist approaches exist for this problem, but to date there has been no Bayesian treatment. In this article we describe Bayesian approaches to modeling a  exible regression function when the predictor variable is mea...

In regression problems involving vector-valued outputs (or equivalently, multiple responses), it is well known that the maximum likelihood estimator (MLE), which takes noise covariance structure into account, can be significantly more accurate than the ordinary least squares (OLS) estimator. However, existing literature compares OLS and MLE in terms of their asymptotic, not finite sample, guara...

Motivation: Gene selection has become a common task in most gene expression studies. The objective of such research is often to identify the smallest possible set of genes that can still achieve good predictive performance. The problem of assigning tumours to a known class is a particularly important example that has received considerable attention in the last ten years. Many of the classificat...

Extreme learning machine (ELM) is an efficient learning algorithm for single-hidden layer feedforward networks (SLFN). This paper proposes the combination of ELM networks using a regularized committee. Simulations on many real-world regression data sets have demonstrated that this algorithm generally outperforms the original ELM algorithm.

Between 10 % and 35 % of all melanomas show histological regression. That is, there is an area within the melanoma where the tumor retreats or disappears to be progressively replaced by fibrosis with presence of melanophages and variable degrees of inflammation, and neovascularization. Such regression is generally considered an indicator of poor prognosis in melanoma, although a number of studi...

The paper considers generalized maximum likelihood asymptotic power one tests which aim to detect a change point in logistic regression when the alternative specifies that a change occurred in parameters of the model. A guaranteed nonasymptotic upper bound for the significance level of each of the tests is presented. For cases in which the test supports the conclusion that there was a change po...