Penalized regression models are popularly used in high-dimensional data analysis to conduct variable selection and model fitting simultaneously. Whereas success has been widely reported in literature, their performances largely depend on the tuning parameters that balance the trade-off between model fitting and model sparsity. Existing tuning criteria mainly follow the route of minimizing the e...

For the standard kernel density estimate, it is known that one can tune the bandwidth such that the expected L1 error is within a constant factor of the optimal L1 error (obtained when one is allowed to choose the bandwidth with knowledge of the density). In this paper, we pose the same problem for variable bandwidth kernel estimates where the bandwidths are allowed to depend upon the location....

The Fuzzy Adaptive Resonance Theory (Fuzzy ART) is an unsupervised neural network and allows both binary and continuous input patterns. In this study, we propose a Fuzzy ART Combining Overlapped Categories Using Variable Vigilance Parameters. The vigilance parameters of the proposed method are arranged for every category, and they are varied according to the size of respective categories with l...