Change-point model selection via AIC
Change-point model selection via AIC
复制标题
通过 AIC 进行变更点模型选择
DOI：
10.1007/s10463-014-0481-x
复制
发表时间：
2015
影响因子：
1
通讯作者：
Y. Ninomiya
中科院分区：
文献类型：
--
作者：
Ogasawara;H.;Y. Ninomiya
变点问题已经被研究了很长时间，不仅因为它们在各个领域都是需要的，而且因为变点模型包含一个不规则性，需要一个替代传统渐近理论。本研究的目的是推导出AIC为这样的变点模型。AIC的惩罚项是最大对数似然的渐近偏差的两倍，而在常规模型中，它是参数数量的两倍。在变点模型中，由于参数的不规则性，它不是参数个数的两倍，其中和分别是变点个数和其他参数个数。在这项研究中，渐近偏差被证明为，这是足够简单的进行一个容易的变点模型选择。此外，AIC的有效性证明了使用模拟研究。
Change-point problems have been studied for a long time not only because they are needed in various fields but also because change-point models contain an irregularity that requires an alternative to conventional asymptotic theory. The purpose of this study is to derive the AIC for such change-point models. The penalty term of the AIC is twice the asymptotic bias of the maximum log-likelihood, whereas it is twice the number of parameters,, in regular models. In change-point models, it is not twice the number of parameters,, because of their irregularity, whereandare the numbers of the change-points and the other parameters, respectively. In this study, the asymptotic bias is shown to become, which is simple enough to conduct an easy change-point model selection. Moreover, the validity of the AIC is demonstrated using simulation studies.