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

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  • Bishwal, J. P. N.

Abstract

Using different random normings, the paper shows that the distributions of the normalized maximum likelihood estimator and normalized regular Bayes estimators of the drift parameter in the Ornstein-Uhlenbeck process observed continuously over [0,T] converge to the standard normal distribution with an error rate O(T-1/2).

Suggested Citation

  • Bishwal, J. P. N., 2001. "Accuracy of normal approximation for the maximum likelihood estimator and Bayes estimators in the Ornstein-Uhlenbeck process using random normings," Statistics & Probability Letters, Elsevier, vol. 52(4), pages 427-439, May.
    • Handle: RePEc:eee:stapro:v:52:y:2001:i:4:p:427-439
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    References listed on IDEAS

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    1. Bishwal, J. P. N., 1999. "Large deviations inequalities for the maximum likelihood estimator and the Bayes estimators in nonlinear stochastic differential equations," Statistics & Probability Letters, Elsevier, vol. 43(2), pages 207-215, June.
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    Cited by:

    1. Jaya P. N. Bishwal, 2010. "Uniform Rate of Weak Convergence of the Minimum Contrast Estimator in the Ornstein–Uhlenbeck Process," Methodology and Computing in Applied Probability, Springer, vol. 12(3), pages 323-334, September.
    2. Bishwal, Jaya P.N., 2006. "Rates of weak convergence of approximate minimum contrast estimators for the discretely observed Ornstein-Uhlenbeck process," Statistics & Probability Letters, Elsevier, vol. 76(13), pages 1397-1409, July.

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