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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 nature of proteins' structure.

SUBMITTER: Zhang T

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

REPOSITORIES: biostudies-literature

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

Zhang Tingting T Kou S C SC

The annals of applied statistics 20100101 4

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 ar ...[more]

PMID: 21258615

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