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Institute of Mathematical Statistics, Annals of Applied Statistics, 4(4), 2010

DOI: 10.1214/10-aoas352

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Nonparametric inference of doubly stochastic Poisson process data via the kernel method

Journal article published in 2010 by Tingting Zhang ORCID, S. C. Kou
This paper is available in a repository.
This paper is available in a repository.

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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. ; Comment: Published in at http://dx.doi.org/10.1214/10-AOAS352 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)