Parametric Inference for Diffusion Processes Observed at Discrete Points in Time: a Survey

By H. Sørensen. 2004

213 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/2117a6db8883983a47791effb0eaa92617dc125d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Finance

In Statistics and Probability

Citations in the corpus

Retrospective exact simulation of diffusion sample paths with applications (2006, background): “Hence, this problem has attracted considerable attention and constitutes a very active area of research; a recent review and references can be found in [ 17 ].”
Monte Carlo Maximum Likelihood Estimation for Discretely Observed Diffusion Processes (2009, background): “, [37] for a recent review) that inference about diffusion models is complicated by the unavailability of the transition density (2), except for limited cases.”
Comment: A Selective Overview of Nonparametric Methods in Financial Econometrics (2005, background): “A recent comprehensive survey of parametric inference for discretely sampled diffusion models can be found in [ 19 ].”
Exact and computationally efficient likelihood‐based estimation for discretely observed diffusion processes (with discussion) (2006, methodology): “An excellent review of several methods of inference for discretely observed diffusions is given in Sørensen (2004).”
Least squares estimator for discretely observed Ornstein-Uhlenbeck processes with small Lévy noises (2009, methodology): “For a comprehensive review on MLE and other related methods, we refer to Sørensen (2004).”
Neural Networks with Cheap Differential Operators (2019, background): “A wide range of parameter estimation approaches have been proposed for SDEs (Prakasa Rao, 1999; Sørensen, 2004; Kutoyants, 2013).”
Hierarchical Models in the Brain (2008, background): “See also Wang and Titterington [31] for a careful analysis of variational Bayes for continuous linear dynamical systems and [32] for a review of the statistical literature on continuous nonlinear dynamical systems.”
DEM: A variational treatment of dynamic systems (2008, methodology): “See alsoWang and Titterington (2004) for a careful analysis of variational Bayes for continuous linear dynamical systems and Sørensen (2004) for a review of the statistical literature on continuous nonlinear dynamical systems.”
Statistical aspects of the fractional stochastic calculus (2006, methodology): “We mention the survey [37] for parameter estimation in discrete cases, further details”
Bayesian inference for irreducible diffusion processes using the pseudo-marginal approach (2011, methodology): “See Sørensen (2004) for a review of inferential methods for diffusions.”
On Simulated Likelihood of Discretely Observed Diffusion Processes and Comparison to Closed-Form Approximation (2007, unknown): “More sophisticated approximation methods fall into four classes (Aït-Sahalia 2002b; Sørensen 2004): (1) Discrete data augmentation at unobserved time points (Pedersen 1995; Elerian et al. 2001; Brandt and Santa-Clara 2002b; Durham and Gallant 2002); (2) Exact simulation approach (Beskos et al.…”
AReS and MaRS - Adversarial and MMD-Minimizing Regression for SDEs (2019, background): “Moreover, Sørensen (2004) and Nielsen et al. (2000) provide extensive explanations of the more traditional approaches.”