ArticleThe annals of applied statistics2010
Nonparametric Inference of Doubly Stochastic Poisson Process Data via the Kernel Method.
Article in The annals of applied statistics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Nonparametric Inference of Doubly Stochastic Poisson Process Data via the Kernel Method.The annals of applied statistics · 2010Article
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Authors and funding
2 authors.
Funding
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.
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