Special issue on variable selection and robust procedures

Special Issue Book Selection

Anyone searching for solid management principles behind the smokescreen of dynamic approaches to strategic management is up against a preliminary difficulty: a large part of the available material ranges from those requiring a familiarity with mathematics, not necessarily available to the average reader, to those conveying ideas that border on common sense. In Kim’s book, behind the clarity of ...

Special Issue on Robust Geometric Algorithms and their Implementations

The robust implementation of geometric algorithms is highly nontrivial. There are several reasons for this: theoretical algorithms (i.e., algorithms on paper) traditionally assume error-free real computations, at constant time cost per operation, and data in general position. All three assumptions do not hold in the real world (i.e., algorithms on silicon). So implementors face the tasks of (1)...

Special Issue on Advances in Data Mining and Robust Statistics

The main aim of data mining is to extract knowledge from, usually very large, datasets. Data mining techniques are often applied to gain initial insights about the data and complement statistical models. This special issue focuses on the interface between data mining and statistical modelling, with special emphasis on robust statistics. Very large datasets, especially those that are machine gen...

Adaptive Robust Variable Selection.

Heavy-tailed high-dimensional data are commonly encountered in various scientific fields and pose great challenges to modern statistical analysis. A natural procedure to address this problem is to use penalized quantile regression with weighted L1-penalty, called weighted robust Lasso (WR-Lasso), in which weights are introduced to ameliorate the bias problem induced by the L1-penalty. In the ul...

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