Semiparametric Multivariate and Multiple Change-Point Modeling
Semiparametric Multivariate and Multiple Change-Point Modeling
复制标题
半参数多元和多变点建模
作者：
S. Peluso;S. Chib;A. Mira
。我们开发了一个通用的贝叶斯半参数变更点模型，其中单独的结构参数组（例如，位置和分散参数）可以遵循由潜在方案之间的时间依赖性过渡矩阵驱动的单独的多个变更点过程。在制度中观察结果的分布尚不清楚，并由Dirichlet工艺混合物提前给出。理论上通过分析到给定时间间隔中的移交时间和变更点的数量来研究所提出的模型的属性。马尔可夫链蒙特卡洛技术进行的先前的后验分析是在一种前回避算法上开发的，用于对各种制度指标进行采样。在各种情况下使用模拟数据的分析以及对短期利率的应用来显示所提出模型的一般性和实用性。
. We develop a general Bayesian semiparametric change-point model in which separate groups of structural parameters (for example, location and dispersion parameters) can each follow a separate multiple change-point process, driven by time-dependent transition matrices among the latent regimes. The distribution of the observations within regimes is unknown and given by a Dirichlet process mixture prior. The properties of the proposed model are studied theoretically through the analysis of inter-arrival times and of the number of change-points in a given time interval. The prior-posterior analysis by Markov chain Monte Carlo techniques is developed on a forward-backward algorithm for sampling the various regime indicators. Analysis with simulated data under various scenarios and an application to short-term interest rates are used to show the generality and usefulness of the proposed model.