Asymptotic equivalence of estimating a Poisson intensity and a positive diffusion drift

DOI10.1214/aos/1028674840zbMath1029.62071OpenAlexW3123120226MaRDI QIDQ1848956

Valentine Genon-Catalot, Michael Nussbaum, Catherine Larédo

Publication date: 14 November 2002

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

Full work available at URL: https://projecteuclid.org/euclid.aos/1028674840

zbMATH Keywords

discretizationdeficiency distancenonparametric experimentsasymptotic minimax constantinverse Gaussian regression

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Density estimation (62G07) Theory of statistical experiments (62B15) Markov processes: estimation; hidden Markov models (62M05)

Related Items (15)

Asymptotic equivalence of discretely observed diffusion processes and their Euler scheme: small variance case ⋮ Goodness of fit test for ergodic diffusions by tick time sample scheme ⋮ Gaussianization machines for non-Gaussian function estimation models ⋮ Asymptotic equivalence for pure jump Lévy processes with unknown Lévy density and Gaussian white noise ⋮ Le cam theory on the comparison of statistical models ⋮ Asymptotic equivalence of nonparametric autoregression and nonparametric regression ⋮ Statistical convergence of Markov experiments to diffusion limits ⋮ The Le Cam distance between density estimation, Poisson processes and Gaussian white noise ⋮ Polynomial bounds in the Ergodic theorem for one-dimensional diffusions and integrability of hitting times ⋮ Realized volatility with stochastic sampling ⋮ Asymptotic equivalence for inhomogeneous jump diffusion processes and white noise ⋮ Asymptotic equivalence of nonparametric diffusion and Euler scheme experiments ⋮ Approximation of epidemic models by diffusion processes and their statistical inference ⋮ Asymptotic nonequivalence of density estimation and Gaussian white noise for small densities ⋮ Asymptotic equivalence and adaptive estimation for robust nonparametric regression

Cites Work

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