Likelihood Inference For Diffusions

Likelihood Inference for Diffusions is a statistical methodology for estimating parameters of diffusion processes by maximizing the likelihood function derived from the transition density of the stochastic differential equation governing the diffusion, distinguished from alternative inference techniques by its reliance on the exact or approximated probability distribution of the continuous-time process over discrete observations.

29

Publications

1.6K

Citations

48

Authors

53

Institutions

Publications per year

2017–2026

4

Authors

48

  1. GO
    Gareth O. Roberts

    University of Warwick, United Kingdom

    3 publications · 408 citations

    3

    h-index

  2. OP
    Omiros Papaspiliopoulos

    Universitat Pompeu Fabra, Spain

    2 publications · 395 citations

    2

    h-index

  3. HL
    Haitao Li

    Cornell University, United States

    2 publications · 115 citations

    2

    h-index

  4. PS
    Pedro Santa‐Clara

    National Bureau of Economic Research, United States

    2 publications · 296 citations

    2

    h-index

  5. AR
    Anders Rahbek

    University of Copenhagen, Denmark

    2 publications · 89 citations

    2

    h-index

1–5 of 48

Venues

  1. arXiv (Cornell University)

    Repository · 3 publications · 45 citations

    3

    h-index

  2. 3

    h-index

  3. Bernoulli

    3 publications · 197 citations

    3

    h-index

  4. Journal of Econometrics

    2 publications · 128 citations

    2

    h-index

  5. Oxford University Research Archive (ORA) (University of Oxford)

    Repository · 2 publications · 54 citations

    2

    h-index

1–5