In real data, there frequently exist some outliers (observations that are markedly different in value from others) derived from, for example, unusual abilities, catastrophe-level phenomena, or human errors. It is difficult to provide a clear definition or threshold of such outliers, moreover, it is effectively impossible to prevent their occurrence. Thus, robust methods that reduce the influence of outliers have a large significance.
My research focuses on robust analytical methods, especially in the model selection problems. I aim to find out a model that can adequately represent phenomena and behavior in a wide range of fields, by utilizing statistical divergence, a measure of farness between probability distributions, to examine the closeness of the underlying “true distribution” and models.