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Bayesian inference for nonlinear multivariate diffusion models observed with error

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  • Golightly, A.
  • Wilkinson, D.J.
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    File URL: http://www.sciencedirect.com/science/article/B6V8V-4NSMMMC-1/2/d202ae99a10d18a335d7c6dcba74ed2e
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    Bibliographic Info

    Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

    Volume (Year): 52 (2008)
    Issue (Month): 3 (January)
    Pages: 1674-1693

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    Handle: RePEc:eee:csdana:v:52:y:2008:i:3:p:1674-1693

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    Web page: http://www.elsevier.com/locate/csda

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    1. Eraker, Bjorn, 2001. "MCMC Analysis of Diffusion Models with Application to Finance," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(2), pages 177-91, April.
    2. Delyon, Bernard & Hu, Ying, 2006. "Simulation of conditioned diffusion and application to parameter estimation," Stochastic Processes and their Applications, Elsevier, vol. 116(11), pages 1660-1675, November.
    3. Elerian, O. & Chib, S. & Shephard, N., 1998. "Likelihood INference for Discretely Observed Non-linear Diffusions," Economics Papers 146, Economics Group, Nuffield College, University of Oxford.
    4. Cox, John C & Ingersoll, Jonathan E, Jr & Ross, Stephen A, 1985. "A Theory of the Term Structure of Interest Rates," Econometrica, Econometric Society, vol. 53(2), pages 385-407, March.
    5. Black, Fischer & Scholes, Myron S, 1973. "The Pricing of Options and Corporate Liabilities," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 637-54, May-June.
    6. Alexandros Beskos & Omiros Papaspiliopoulos & Gareth O. Roberts & Paul Fearnhead, 2006. "Exact and computationally efficient likelihood-based estimation for discretely observed diffusion processes (with discussion)," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(3), pages 333-382.
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    Cited by:
    1. Isambi Mbalawata & Simo Särkkä & Heikki Haario, 2013. "Parameter estimation in stochastic differential equations with Markov chain Monte Carlo and non-linear Kalman filtering," Computational Statistics, Springer, vol. 28(3), pages 1195-1223, June.
    2. Beskos, Alexandros & Kalogeropoulos, Konstantinos & Pazos, Erik, 2013. "Advanced MCMC methods for sampling on diffusion pathspace," Stochastic Processes and their Applications, Elsevier, vol. 123(4), pages 1415-1453.
    3. Yuan Shen & Dan Cornford & Manfred Opper & Cedric Archambeau, 2012. "Variational Markov chain Monte Carlo for Bayesian smoothing of non-linear diffusions," Computational Statistics, Springer, vol. 27(1), pages 149-176, March.
    4. Pedersen, M.W. & Thygesen, U.H. & Madsen, H., 2011. "Nonlinear tracking in a diffusion process with a Bayesian filter and the finite element method," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 280-290, January.
    5. Joseph Dureau & Konstantinos Kalogeropoulos & Marc Baguelin, 2013. "Capturing the time-varying drivers of an epidemic using stochastic dynamical systems," LSE Research Online Documents on Economics 41749, London School of Economics and Political Science, LSE Library.
    6. Carl Chiarella & Thuy-Duong To, 2005. "The Volatility Structure of the Fixed Income Market under the HJM Framework: A Nonlinear Filtering Approach," Research Paper Series 150, Quantitative Finance Research Centre, University of Technology, Sydney.

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