Nonhomogeneous hidden semi-Markov models for toroidal data
arxiv(2023)
摘要
A nonhomogeneous hidden semi-Markov model is proposed to segment toroidal
time series according to a finite number of latent regimes and, simultaneously,
estimate the influence of time-varying covariates on the process' survival
under each regime. The model is a mixture of toroidal densities, whose
parameters depend on the evolution of a semi-Markov chain, which is in turn
modulated by time-varying covariates through a proportional hazards assumption.
Parameter estimates are obtained using an EM algorithm that relies on an
efficient augmentation of the latent process. The proposal is illustrated on a
time series of wind and wave directions recorded during winter.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要