.png){kind=icon}
返回
Exact Monte Carlo likelihood-based inference for jump-diffusion processes
.png){kind=icon}
OA
AI
DOI：10.1093/jrsssb/qkad022.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Statistical inference for discretely observed jump-diffusion processes is a complex problem which motivates new methodological challenges. Thus, existing approaches invariably resort to time-discretisations which inevitably lead to approximations in inference. In this paper, we give the first general collection of methodologies for exact (in this context meaning discretisation-free) likelihood-based inference for discretely observed finite activity jump-diffusions. The only sources of error involved are Monte Carlo error and convergence of expectation maximisation (EM) or Markov chain Monte Carlo (MCMC) algorithms. We shall introduce both frequentist and Bayesian approaches, illustrating the methodology through simulated and real examples.
Keyword:
Barker's algorithm
MCEM
MCMC
Poisson estimator
retrospective sampling
期刊
J
IF:
3.6
论文数:
1.5K
被引数:
3.2W
机构
引用论文
Interpreting Geoheritage at New Zealand’s Geothermal Tourist Sites—Systematic Explanation Versus Storytelling.png)
Geoheritage
GeoheritageIF0
PREAI
分享收藏Use of reduced-energy content maintenance diets for modest weight reduction in overweight cats and dogs使用低能量含量的维持饮食对超重猫和狗进行适度体重减轻
PREAI
分享收藏Exact and computationally efficient likelihood-based estimation for discretely observed diffusion processes (with discussion)离散观察到的扩散过程的精确且计算效率高的基于似然的估计 (带讨论)
OAAI
OAAI分享收藏
查看更多内容