Product Partition Dynamic Generalized Linear Models release_naeyt2n6rbe7vgybkcpxujgrvi

by Victor S. Comitti, Fábio N. Demarqui, Thiago R. dos Santos, Jéssica da Assunção Almeida

Released as a article .

2021  

Abstract

Detection and modeling of change-points in time-series can be considerably challenging. In this paper we approach this problem by incorporating the class of Dynamic Generalized Linear Models (DGLM) into the well know class of Product Partition Models (PPM). This new methodology, that we call DGLM-PPM, extends the PPM to distributions within the Exponential Family while also retaining the flexibility of the DGLM class. It also provides a framework for Bayesian multiple change-point detection in dynamic regression models. Inference on the DGLM-PPM follow the steps of evolution and updating of the DGLM class. A Gibbs Sampler scheme with an Adaptive Rejection Metropolis Sampling (ARMS) step appended is used to compute posterior estimates of the relevant quantities. A simulation study shows that the proposed model provides reasonable estimates of the dynamic parameters and also assigns high change-point probabilities to the breaks introduced in the artificial data generated for this work. We also present a real life data example that highlights the superiority of the DGLM-PPM over the conventional DGLM in both in-sample and out-of-sample goodness of fit measures.
In text/plain format

Archived Files and Locations

application/pdf  867.4 kB
file_5bk4t3p4vjhjlkgv33s6eqpbuq
arxiv.org (repository)
web.archive.org (webarchive)
Read Archived PDF
Preserved and Accessible
Type  article
Stage   submitted
Date   2021-03-03
Version   v1
Language   en ?
arXiv  2103.02470v1
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: 76a8d26d-e431-43ec-bf7e-948bd4bee870
API URL: JSON