Posterior contraction rates for support boundary recovery

Markus Reiß and Johannes Schmidt-Hieber

Stochastic Processes and their Applications, 2020, vol. 130, issue 11, 6638-6656

Abstract: Given a sample of a Poisson point process with intensity λf(x,y)=n1(f(x)≤y), we study recovery of the boundary function f from a nonparametric Bayes perspective. Because of the irregularity of this model, the analysis is non-standard. We establish a general result for the posterior contraction rate with respect to the L1-norm based on entropy and one-sided small probability bounds. From this, specific posterior contraction results are derived for Gaussian process priors and priors based on random wavelet series.

Keywords: Frequentist Bayesian analysis; Posterior contraction; Poisson point process; Boundary detection; Gaussian prior; Wavelet prior (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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DOI: 10.1016/j.spa.2020.06.005

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