Stochastic processes, a type of continuous-time model, are used in a wide variety of fields, including financial engineering. We are working on developing a statistical theory for modeling deviations from the normal distribution of data, which are widely observed in such fields. In particular, we focus on high-frequency observations with a very fine observation time range. Our goal is to provide a new guideline for extracting information from stochastic processes with various distributional characteristics.

The development of measuring instruments and computers has made it possible to obtain very large data sets.
Statistics is indispensable for extracting information from such large-scale data and making appropriate decisions.
In this laboratory, we deal with statistical theory for analyzing data obtained from time-evolving phenomena (time series data), and at the same time, we are developing simulation methods.