infsp.bib

@comment{{This file has been generated by bib2bib 1.96}}

@comment{{Command line: bib2bib -ob infsp.bib -s year -c select:"inf.s.p" Omiros_refs.bib}}

@article{robe:papa:dell:2004,
  author = {Roberts, Gareth O. and Papaspiliopoulos, Omiros and
              Dellaportas, Petros},
  title = {Bayesian inference for non-{G}aussian {O}rnstein-{U}hlenbeck
              stochastic volatility processes},
  journal = {J. R. Stat. Soc. Ser. B Stat. Methodol.},
  fjournal = {Journal of the Royal Statistical Society. Series B.
              Statistical Methodology},
  volume = {66},
  year = {2004},
  number = {2},
  pages = {369--393},
  issn = {1369-7412},
  mrclass = {62M05},
  mrnumber = {2062382},
  url = {https://dx.doi.org/10.1111/j.1369-7412.2004.05139.x},
  select = {inf.s.p},
  abstract = {We develop Markov chain Monte Carlo methodology for Bayesian inference for non-Gaussian Ornstein-Uhlenbeck stochastic volatility processes. The approach introduced involves expressing the unobserved stochastic volatility process in terms of a suitable marked Poisson process. We introduce two specific classes of Metropolis-Hastings algorithms which correspond to different ways of jointly parameterizing the marked point process and the model parameters. The performance of the methods is investigated for different types of simulated data. The approach is extended to consider the case where the volatility process is expressed as a superposition of Ornstein–Uhlenbeck processes. We apply our methodology to the US dollar-Deutschmark exchange rate.},
  keywords = {Data augmentation; L\'evy processes; Marked point processes; Markov chain Monte Carlo methods; Non-centred parameterizations; Stochastic volatility}
}

@article{besk:papa:robe:fear:2006,
  author = {Beskos, Alexandros and Papaspiliopoulos, Omiros and Roberts,
              Gareth O. and Fearnhead, Paul},
  title = {Exact and computationally efficient likelihood-based
              estimation for discretely observed diffusion processes},
  note = {With discussions and a reply by the authors},
  journal = {J. R. Stat. Soc. Ser. B Stat. Methodol.},
  fjournal = {Journal of the Royal Statistical Society. Series B.
              Statistical Methodology},
  volume = {68},
  year = {2006},
  number = {3},
  pages = {333--382},
  issn = {1369-7412},
  mrclass = {62M05 (62F10)},
  mrnumber = {2278331},
  url = {https://dx.doi.org/10.1111/j.1467-9868.2006.00552.x},
  select = {inf.s.p},
  abstract = { The objective of the paper is to present a novel methodology for likelihood-based inference for discretely observed diffusions. We propose Monte Carlo methods, which build on recent advances on the exact simulation of diffusions, for performing maximum likelihood and Bayesian estimation.},
  keywords = {Cox-Ingersoll-Ross model;
    EM algorithm;
    Graphical models;
    Markov chain Monte Carlo methods;
    Monte Carlo maximum likelihood;
    Retrospective sampling}
}

@article{besk:papa:robe:mcmle,
  author = {Beskos, Alexandros and Papaspiliopoulos, Omiros and Roberts,
              Gareth},
  title = {Monte {C}arlo maximum likelihood estimation for discretely
              observed diffusion processes},
  journal = {Ann. Statist.},
  fjournal = {The Annals of Statistics},
  volume = {37},
  year = {2009},
  number = {1},
  pages = {223--245},
  issn = {0090-5364},
  coden = {ASTSC7},
  mrclass = {62M05 (65C05)},
  mrnumber = {2488350 (2010c:62259)},
  mrreviewer = {Bernd Heidergott},
  url = {https://dx.doi.org/10.1214/07-AOS550},
  select = {inf.s.p},
  abstract = {This paper introduces a Monte Carlo method for maximum likelihood inference in the context of discretely observed diffusion processes. The method gives unbiased and a.s. continuous estimators of the likelihood function for a family of diffusion models and its performance in numerical examples is computationally efficient. It uses a recently developed technique for the exact simulation of diffusions, and involves no discretization error. We show that, under regularity conditions, the Monte Carlo MLE converges a.s. to the true MLE. For datasize $n \to \infty$, we show that the number of Monte Carlo iterations should be tuned as $\mathcal{O}(n^{1/2})$ and we demonstrate the consistency properties of the Monte Carlo MLE as an estimator of the true parameter value. },
  keywords = {Coupling; uniform convergence; exact simulation; linear diffusion processes; random function; SLLN on Banach space}
}

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