While robust divergence such as density power and $\gamma$-divergence is helpful for statistical inference in the presence of outliers, tuning parameter that controls degree robustness chosen a rule-of-thumb, which may lead to an inefficient inference. We here propose selection criterion based on asymptotic approximation Hyvarinen score applied unnormalized model defined by divergence. The prop...

The use of Transformer-based pre-trained language models has become prevalent in enhancing the performance task-oriented dialogue systems. These models, which are on large text data to grasp syntax and semantics, fine-tune entire parameter set according a specific task. However, as scale model increases, several challenges arise during fine-tuning process. For example, training time escalates g...

We consider the problem of component selection in a functional ANOVA model. A nonparametric extension of the nonnegative garrote (Breiman, 1996) is proposed. We show that the whole solution path of the proposed method can be efficiently computed, which, in turn , facilitates the selection of the tuning parameter. We also show that the final estimate enjoys nice theoretical properties given that...