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Modeling count time series: a comparative case study

被引：0
|
作者：
Maia, Gisele O. [1 ]
Franco, Glaura C. [1 ]
Santos, Thiago R. [1 ]
Camara, Ana Julia A. [2 ]
机构：
[1] Univ Fed Minas Gerais UFMG, Dept Estat, Belo Horizonte, Brazil
[2] Univ Fed Espirito Santo UFES, Dept Estat, Espirito Santo, Brazil
来源：
SIGMAE | 2024年 / 13卷 / 01期
关键词：
Observation-driven model; Parameter-driven model; GAM-ARMA; NGSSEML; count data; DRIVEN; REGRESSION;
D O I：
暂无
中图分类号：
O1 [数学];
学科分类号：
0701 ; 070101 ;
摘要：
This paper presents an application for counting data, where the observation-driven and parameter -driven models are compared. To this purpose, the Generalized Additive Autoregressive Moving Average (GAM -ARMA) and Non-Gaussian State Space with Exact Marginal Likelihood (NGSSEML) models are used. Model parameters are estimated using the maximum likelihood method. The ability of the procedure to model and forecast real data is presented for the number of chronic obstructive disease (COPD) cases.
引用
收藏
页码：13 / 23
页数：11
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