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

DOI10.1214/10-AOAS352zbMath1220.62037arXiv1101.1210WikidataQ30433448 ScholiaQ30433448MaRDI QIDQ542959

S. C. Kou, Ting-Ting Zhang

Publication date: 20 June 2011

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1101.1210

zbMATH Keywords

asymptotic normalityautocorrelation functionbandwidth selectionCox processshort-range dependencearrival ratebiophysical experiments

Mathematics Subject Classification ID

Density estimation (62G07) Asymptotic distribution theory in statistics (62E20) Non-Markovian processes: estimation (62M09) Applications of statistics to physics (62P35) Biophysics (92C05)

Related Items (6)

Statistical methodology in single-molecule experiments ⋮ Nonparametric tests for Cox processes ⋮ Nonparametric inference of doubly stochastic Poisson process data via the kernel method ⋮ Estimating doubly stochastic Poisson process with affine intensities by Kalman filter ⋮ A selective view of stochastic inference and modeling problems in nanoscale biophysics ⋮ Nonparametric intensity estimation from noisy observations of a Poisson process under unknown error distribution

Uses Software

• KernSmooth
• sm

Cites Work

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