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The consistency of a non-linear least squares estimator from diffusion processes

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  • Kasonga, R. A.

Abstract

Consider the following Itô stochastic differential equation dX(t) = [latin small letter f with hook]([theta]0, X(t)) dt + dW(t), where (W(t), t [greater-or-equal, slanted] 0), is a standard Wiener process in RN. On the basis of discrete data 0 = t0

Suggested Citation

  • Kasonga, R. A., 1988. "The consistency of a non-linear least squares estimator from diffusion processes," Stochastic Processes and their Applications, Elsevier, vol. 30(2), pages 263-275, December.
    • Handle: RePEc:eee:spapps:v:30:y:1988:i:2:p:263-275
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    Cited by:

    1. Andreas Neuenkirch & Samy Tindel, 2014. "A least square-type procedure for parameter estimation in stochastic differential equations with additive fractional noise," Statistical Inference for Stochastic Processes, Springer, vol. 17(1), pages 99-120, April.
    2. Bishwal Jaya P. N., 2009. "Berry–Esseen inequalities for discretely observed diffusions," Monte Carlo Methods and Applications, De Gruyter, vol. 15(3), pages 229-239, January.
    3. Yasutaka Shimizu, 2012. "Local asymptotic mixed normality for discretely observed non-recurrent Ornstein–Uhlenbeck processes," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 64(1), pages 193-211, February.
    4. Shimizu, Yasutaka, 2009. "Notes on drift estimation for certain non-recurrent diffusion processes from sampled data," Statistics & Probability Letters, Elsevier, vol. 79(20), pages 2200-2207, October.

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