| Grant number: | 17/22914-5 |
| Support Opportunities: | Scholarships abroad - Research Internship - Doctorate |
| Start date: | May 01, 2018 |
| End date: | April 30, 2019 |
| Field of knowledge: | Physical Sciences and Mathematics - Probability and Statistics - Statistics |
| Principal Investigator: | Hedibert Freitas Lopes |
| Grantee: | Helton Graziadei de Carvalho |
| Supervisor: | Igor Pruenster |
| Host Institution: | Instituto de Matemática e Estatística (IME). Universidade de São Paulo (USP). São Paulo , SP, Brazil |
| Institution abroad: | Università Commerciale Luigi Bocconi, Italy |
| Associated to the scholarship: | 17/10096-6 - Bayesian semiparametric analysis of autoregressive models, BP.DR |
Abstract In this project, we propose extensions of the INAR(1) model by using nonparametric priors on the time-varying innovations distributions. We first investigate clustering aspects and the predictive performance of the Dirichlet Process Mixture (DPM) model. In order to overcome limitations of the DPMs, we introduce more general non-parametric priors based on Pitman-Yor Processes and Gibbs-Type Measures. These generalizations facilitate the estimation of the posterior distribution of clusters and may improve predictive performance in a wide range of applications. Moreover, we explore the predictive performance of the proposed models in overdispersed datasets. (AU) | |
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