Modal Principal Component Analysis

Principal component analysis (PCA) is a widely used method for data processing, such as for dimension reduction and visualization. Standard PCA is known to be sensitive to outliers, and various robust PCA methods have been proposed. It has been shown that the robustness of many statistical methods can be improved using mode estimation instead of mean estimation, because mode estimation is not significantly affected by the presence of outliers. Thus, this study proposes a modal principal component analysis (MPCA), which is a robust PCA method based on mode estimation. The proposed method finds the minor component by estimating the mode of the projected data points. As a theoretical contribution, probabilistic convergence property, influence function, finite-sample breakdown point, and its lower bound for the proposed MPCA are derived. The experimental results show that the proposed method has advantages over conventional methods.

Efficient robust doubly adaptive regularized regression with applications

We consider the problem of estimation and variable selection for general linear regression models. Regularized regression procedures have been widely used for variable selection, but most existing methods perform poorly in the presence of outliers. We construct a new penalized procedure that simultaneously attains full efficiency and maximum robustness. Furthermore, the proposed procedure satisfies the oracle properties. The new procedure is designed to achieve sparse and robust solutions by imposing adaptive weights on both the decision loss and the penalty function. The proposed method of estimation and variable selection attains full efficiency when the model is correct and, at the same time, achieves maximum robustness when outliers are present. We examine the robustness properties using the finite-sample breakdown point and an influence function. We show that the proposed estimator attains the maximum breakdown point. Furthermore, there is no loss in efficiency when there are no outliers or the error distribution is normal. For practical implementation of the proposed method, we present a computational algorithm. We examine the finite-sample and robustness properties using Monte Carlo studies. Two datasets are also analyzed.

An Overview on the Statistical Central Tendency for Fuzzy Data Sets

When Statistics deals with data which cannot be expressed in a numerical scale, the scale of fuzzy values (in particular, the scale of fuzzy numbers) often becomes a suitable tool to express such data. In this way, many ratings, opinions, judgements, etc. mostly coming from human valuations can be appropriately described in terms of fuzzy data. To summarize the central tendency of a fuzzy dataset, some measures have been suggested in the literature. This paper aims to review some of the main ones, and examine their properties in a comparative way. A real-life example illustrates their application. Furthermore, the paper shows the statistical robustness (both through the finite sample breakdown point and a simulation study) and the empirical “precision” of the fuzzy number-valued sample measures. Finally, some related developments and future directions are pointed out.