Maximum likelihood inference for a class of discrete-time Markov-switching time series models with multiple delays
多重时滞离散时间马尔可夫切换模型的最大似然推断
Autoregressive Markov switching (ARMS) time series models are used to represent real-world signals whose dynamics may change over time. They have found application in many areas of the natural and social sciences, as well as in engineering. In general, inference in this kind of systems involves two problems: (a) detecting the number of distinct dynamical models that the signal may adopt and (b) estimating any unknown parameters in these models. In this paper, we introduce a…