Nonparametric Inference of Doubly Stochastic Poisson Process Data via the Kernel Method.

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 n

SUBMITTER: Zhang T 

PROVIDER: S-EPMC3024001 | biostudies-literature | 2010 Jan

REPOSITORIES: biostudies-literature