A regularized bridge sampler for sparsely sampled diffusions
From MaRDI portal
Publication:746239
DOI10.1007/s11222-011-9255-yzbMath1322.62211OpenAlexW1985954067MaRDI QIDQ746239
Publication date: 16 October 2015
Published in: Statistics and Computing (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s11222-011-9255-y
Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Sampling theory, sample surveys (62D05) Diffusion processes (60J60)
Related Items
A penalized simulated maximum likelihood approach in parameter estimation for stochastic differential equations, Efficient computation of the quasi likelihood function for discretely observed diffusion processes, Stability of sampling proposals for reducible diffusions over large time intervals, A penalized simulated maximum likelihood method to estimate parameters for SDEs with measurement error, Bayesian analysis of ambulatory blood pressure dynamics with application to irregularly spaced sparse data, Improved bridge constructs for stochastic differential equations, Simulation of elliptic and hypo-elliptic conditional diffusions, Continuous-discrete smoothing of diffusions, Zero-inflated regime-switching stochastic differential equation models for highly unbalanced multivariate, multi-subject time-series data
Cites Work
- Unnamed Item
- Unnamed Item
- Testing the assumptions behind importance sampling
- A two-stage realized volatility approach to estimation of diffusion processes with discrete data
- Monte Carlo maximum likelihood estimation for discretely observed diffusion processes
- Bayesian inference for nonlinear multivariate diffusion models observed with error
- Consistency and asymptotic normality of an approximate maximum likelihood estimator for discretely observed diffusion processes
- Implications of parameter uncertainty on option prices
- Closed-form likelihood expansions for multivariate diffusions
- Estimating parameters in diffusion processes using an approximate maximum likelihood approach
- Exact and Computationally Efficient Likelihood-Based Estimation for Discretely Observed Diffusion Processes (with Discussion)
- Likelihood Inference for Discretely Observed Nonlinear Diffusions
- Deterministic Nonperiodic Flow
- Bayesian Inference in Econometric Models Using Monte Carlo Integration
- A Closed-Form Solution for Options with Stochastic Volatility with Applications to Bond and Currency Options
- Modeling and Forecasting Realized Volatility
- Maximum Likelihood Estimation of Discretely Sampled Diffusions: A Closed-form Approximation Approach
- Stochastic differential equations. An introduction with applications.
