Swarm-based algorithms emerged as a powerful family of optimization techniques, inspired
by the collective behavior of social animals. In particle swarm optimization (PSO) the set of …

Industrial process data are usually mixed with missing data and outliers which can greatly
affect the statistical explanation abilities for traditional data-driven modeling methods. In this …

Principal component analysis (PCA) is one of the most widely used dimension reduction
techniques. A related easier problem is termed subspace learning or subspace estimation …

Robust principal component analysis (RPCA) via decomposition into low-rank plus sparse
matrices offers a powerful framework for a large variety of applications such as image …

A new edition of this popular text on robust statistics, thoroughly updated to include new and
improved methods and focus on implementation of methodology using the increasingly …

The viewpoint taken in much of this text is that PCA is mainly a descriptive tool with no need
for rigorous distributional or model assumptions. This implies that it can be used on a wide …

We introduce a new method for robust principal component analysis (PCA). Classical PCA is
based on the empirical covariance matrix of the data and hence is highly sensitive to …

A new method is proposed for forecasting age-specific mortality and fertility rates observed
over time. This approach allows for smooth functions of age, is robust for outlying years due …

Taking advantage of the S4 class system of the programming environment R, which
facilitates the creation and maintenance of reusable and modular components, an object …

The limited coverage of data analysis and statistics offered in most undergraduate and
graduate analytical chemistry courses is usually focused on practical aspects of univariate …