Bayesian comparative study on binary time series

被引：0

作者：

Paul, Erina ^{[1
,4
]}

Maity, Arnab Kumar ^{[2
]}

Maiti, Raju ^{[3
]}

机构：

[1] Grand Valley State Univ, Allendale, MI 49401 USA

[2] Texas A&M Univ, College Stn, TX 77843 USA

[3] Duke NUS Med Sch, Singapore, Singapore

[4] Wayne State Univ, Detroit, MI USA

来源：

JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION | 2018年 / 88卷 / 14期

关键词：

DIC; Jeffreys prior; log-marginal likelihood; misclassification error rate; normal prior; prediction; Student's t prior; 62F15; AUTOREGRESSIVE MODEL;

D O I：

10.1080/00949655.2018.1488256

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

In this paper, we consider the Bayesian analysis of binary time series with different priors, namely normal, Students' t, and Jeffreys prior, and compare the results with the frequentist methods through some simulation experiments and one real data on daily rainfall in inches at Mount Washington, NH. Among Bayesian methods, our results show that the Jeffreys prior perform better in most of the situations for both the simulation and the rainfall data. Furthermore, among weakly informative priors considered, Student's t prior with 7 degrees of freedom fits the data most adequately.

引用

页码：2811 / 2826

页数：16

共 40 条

[1] Bayesian dynamic modeling of time series of dengue disease case counts [J].