Accuracy of normal approximation for the maximum likelihood estimator and Bayes estimators in the Ornstein-Uhlenbeck process using random normings

DOI10.1016/S0167-7152(01)00026-8zbMath0989.62044MaRDI QIDQ5951996

Jaya P. N. Bishwal

Publication date: 2 July 2002

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

zbMATH Keywords

Ornstein-Uhlenbeck processmaximum likelihood estimatorBayes estimatorsIto stochastic differential equationrandom normingsrate of weak convergence

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Central limit and other weak theorems (60F05) Bayesian inference (62F15) Markov processes: estimation; hidden Markov models (62M05) Diffusion processes (60J60)

Related Items (3)

Hypotheses testing about the drift parameter in linear stochastic differential equation driven by stable processes ⋮ Uniform rate of weak convergence of the minimum contrast estimator in the Ornstein-Uhlenbeck process ⋮ Rates of weak convergence of approximate minimum contrast estimators for the discretely observed Ornstein-Uhlenbeck process

Cites Work

• On the effect of random norming on the rate of convergence in the central limit theorem
• Large deviations inequalities for the maximum likelihood estimator and the Bayes estimators in nonlinear stochastic differential equations
• The Berry-Esseen bound for minimum contrast estimates
• On the Rate of Convergence for the Invariance Principle
• Maximum likelihood estimation for continuous-time stochastic processes
• Speed of Convergence of the Maximum Likelihood Estimator in the Ornstein-Uhlenbeck Process
• Large deviations in estimation of an Ornstein-Uhlenbeck model
• Rates of convergence of the posterior distributions and the Bayes estimations in the Ornstein-Uhlenbeck process
• The accuracy of the normal approximation for minimum contrast estimates
• Rates of convergence of approximate maximum likelihood estimators in the Ornstein-Uhlenbeck process
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