Hybrid estimators for stochastic differential equations from reduced data
Hybrid estimators for stochastic differential equations from reduced data
复制标题
来自简化数据的随机微分方程的混合估计器
DOI：
10.1007/s11203-018-9184-x
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发表时间：
2018
影响因子：
0.8
通讯作者：
Yusuke Kaino and Masayuki Uchida
中科院分区：
文献类型：
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作者：
Tamura Yoichi;Mawatari Ken;Hashimoto Takuya;Inoue Akio K.;Zackrisson Erik;Christensen Lise;Binggeli Christian;Matsuda Yuichi;Matsuo Hiroshi;Takeuchi Tsutomu T.;Asano Ryosuke S.;Sunaga Kaho;Shimizu Ikkoh;Okamoto Takashi;Yoshida Naoki;et al.;Yusuke Kaino and Masayuki Uchida
本文从计算代价的观点出发，讨论了由离散观测值对随机微分方程未知参数的参数推断问题。继Kamatani等人（Bull Inf Cybern 48：19-35，2016）和Kaino和Uchida（稀疏数据中遍历扩散过程的混合估计量，2018）之后，我们给出了遍历和非遍历扩散类型过程的初始贝叶斯类型估计量的多步估计量的渐近结果。利用从完整数据中获得的约化数据和稀疏数据来构造初始贝叶斯类型估计量。文中还给出了一些算例和仿真结果。
We treat parametric inference for unknown parameters of stochastic differential equations from discrete observations from the viewpoint of computational cost. Following Kamatani et al. (Bull Inf Cybern 48:19–35, 2016) and Kaino and Uchida (Hybrid estimators for ergodic diffusion processes from thinned data, 2018), we present the asymptotic results of the multi-step estimators with the initial Bayes type estimators for both ergodic and non-ergodic diffusion type processes. The initial Bayes type estimators are constructed by means of both the reduced data and the thinned data obtained from the full data. Some examples and simulation results are also given.