Likelihood Inference for Exponential-Trawl Processes
Likelihood Inference for Exponential-Trawl Processes
复制标题
指数拖网过程的似然推断
DOI：
10.1007/978-3-319-25826-3_12
复制
发表时间：
2015
期刊：
影响因子：
--
通讯作者：
Justin Yang
中科院分区：
文献类型：
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作者：
N. Shephard;Justin Yang
整数拖网过程是Ole E. Barndorff-Nielsen近年来研究的一类序列相关、平稳、无限可分的过程。在本章中，我们通过关注所谓的指数-拖网过程，首次分析了拖网过程的似然推理，这也是一个具有可数状态空间的连续时间隐马尔可夫过程。其核心思想包括预测分解、滤波平滑、全数据分析和EM算法。这些可以很容易地扩展以适应更一般的拖网过程，但需要增加计算工作量。
Integer-valued trawl processes are a class of serially correlated, stationary and infinitely divisible processes that Ole E. Barndorff-Nielsen has been working on in recent years. In this chapter, we provide the first analysis of likelihood inference for trawl processes by focusing on the so-called exponential-trawl process, which is also a continuous time hidden Markov process with countable state space. The core ideas include prediction decomposition, filtering and smoothing, complete-data analysis and EM algorithm. These can be easily scaled up to adapt to more general trawl processes but with increasing computation efforts.