Model selection for Poisson processes

DOI10.1214/074921707000000265zbMath1176.62082arXivmath/0609549OpenAlexW1766174720MaRDI QIDQ5324530

Lucien Birgé

Publication date: 3 August 2009

Published in: Institute of Mathematical Statistics Lecture Notes - Monograph Series (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/math/0609549

zbMATH Keywords

aggregationmodel selectionadaptive estimationrobust testsintensity estimation

Mathematics Subject Classification ID

Density estimation (62G07) Asymptotic properties of nonparametric inference (62G20) Linear regression; mixed models (62J05) Nonparametric estimation (62G05) Non-Markovian processes: estimation (62M09) Markov processes: estimation; hidden Markov models (62M05) Approximation by arbitrary nonlinear expressions; widths and entropy (41A46) Approximation by arbitrary linear expressions (41A45)

Related Items (14)

Robust estimation on a parametric model via testing ⋮ Estimating linear functionals of a sparse family of Poisson means ⋮ Estimating the conditional density by histogram type estimators and model selection ⋮ Estimation of the transition density of a Markov chain ⋮ Concentration inequalities for Poisson point processes with application to adaptive intensity estimation ⋮ Near optimal thresholding estimation of a Poisson intensity on the real line ⋮ Histogram selection for possibly censored data ⋮ Estimator selection with respect to Hellinger-type risks ⋮ Model selection for density estimation with \(\mathbb L_2\)-loss ⋮ Model selection for Poisson processes with covariates ⋮ Can one hear the shape of a population history? ⋮ Estimating the intensity of a random measure by histogram type estimators ⋮ Nonparametric intensity estimation from noisy observations of a Poisson process under unknown error distribution ⋮ Aggregating estimates by convex optimization

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