Nonparametric Inference of Doubly Stochastic Poisson Process Data via the Kernel Method |
| |
Authors: | Zhang Tingting Kou S C |
| |
Institution: | Department of Statistics, University of Virginia, Charlottesville, VA 22904 ( tz3b@virginia.edu ). |
| |
Abstract: | Doubly stochastic Poisson processes, also known as the Cox processes, frequently occur in various scientific fields. In this article, motivated primarily by analyzing Cox process data in biophysics, we propose a nonparametric kernel-based inference method. We conduct a detailed study, including an asymptotic analysis, of the proposed method, and provide guidelines for its practical use, introducing a fast and stable regression method for bandwidth selection. We apply our method to real photon arrival data from recent single-molecule biophysical experiments, investigating proteins' conformational dynamics. Our result shows that conformational fluctuation is widely present in protein systems, and that the fluctuation covers a broad range of time scales, highlighting the dynamic and complex nature of proteins' structure. |
| |
Keywords: | |
本文献已被 PubMed 等数据库收录! |
|