The focus is on robust regression methods for problems where the predictor matrix has full rank and where it is rank deficient. For the first situation, various robust regression methods have been introduced, and here an overview of the most important proposals is given. For the latter case, robust partial least squares regression is discussed. The way of downweighting outlying observations is ...

Genomic selection involves computing a prediction equation from the estimated effects of a large number of DNA markers based on a limited number of genotyped animals with phenotypes. The number of observations is much smaller than the number of independent variables, and the challenge is to find methods that perform well in this context. Partial least squares regression (PLS) and sparse PLS wer...

Dimension reduction is important during the analysis of gene expression microarray data, because the high dimensionality of data sets hurts the generalization performance of classifiers. Partial Least Squares (PLS) based dimension reduction is a frequently used method, since it is specialized in handling high dimensional data set and leads to satisfying classification performance. This paper in...

This research examines the complex temporal structures that individual professionals experience and manage in their electronic calendar tools. Its focus is on understanding the relationship between the quality of individual time management and an individual’s understanding and use of temporal structures afforded by electronic calendar systems. This work consists of a field study which examines ...

Microarray data usually contain missing values, thus estimating these missing values is an important preprocessing step. This paper proposes an estimation method of missing values based on Partial Least Squares (PLS) regression. The method is feasible for microarray data, because of the characteristics of PLS regression. We compared our method with three methods, including ROWaverage, KNNimpute...