Adaptive invariant density estimation for continuous-time mixing Markov processes under sup-norm risk
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Publication:2083865
DOI10.1214/21-AIHP1235OpenAlexW4303045786MaRDI QIDQ2083865
Claudia Strauch, Niklas Dexheimer, Lukas Trottner
Publication date: 11 October 2022
Published in: Annales de l'Institut Henri Poincaré. Probabilités et Statistiques (Search for Journal in Brave)
Full work available at URL: https://projecteuclid.org/journals/annales-de-linstitut-henri-poincare-probabilites-et-statistiques/volume-58/issue-4/Adaptive-invariant-density-estimation-for-continuous-time-mixing-Markov-processes/10.1214/21-AIHP1235.full
adaptive estimationnonparametric statisticsjump SDEsstatistics for stochastic processessup-norm risk
Asymptotic properties of nonparametric inference (62G20) Nonparametric estimation (62G05) Stationary stochastic processes (60G10) Markov processes: estimation; hidden Markov models (62M05) Continuous-time Markov processes on general state spaces (60J25)
Related Items
Stability of overshoots of Markov additive processes, Sup-norm adaptive drift estimation for multivariate nonreversible diffusions
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