Exact and Computationally Efficient Likelihood-Based Estimation for Discretely Observed Diffusion Processes (with Discussion) 论文
2006Journal of the Royal Statistical Society Series B (Statistical Methodology)引用 356
Statistical Methods and InferenceProbabilistic and Robust Engineering DesignBayesian Methods and Mixture Models
摘要
Summary 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.