PAR(1) model analysis: a web-based shiny application for analysing periodic autoregressive models

In this paper, a web-based shiny application called the 'PAR(1) Model Analysis'- that allows the modelling, estimation and prediction of a periodic autoregressive time series with scale mixtures of skew-normal innovations, a general and quite flexible class of error distributions-is presen...

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Bibliographic Details
Published inJournal of statistical computation and simulation Vol. 92; no. 10; pp. 2090 - 2111
Main Authors Manouchehri, T., Nematollahi, A. R.
Format Journal Article
LanguageEnglish
Published Abingdon Taylor & Francis 03.07.2022
Taylor & Francis Ltd
Subjects
62M10: 62H12
Applications programs
Autoregressive models
Bayesian analysis
Data analysis
ECM algorithms
Estimation
Gibbs sampling
MAP estimate
MCMC algorithms
Mixtures
periodic autoregressive models
Scale mixture of skew normal
shiny
Skewed distributions
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Summary:In this paper, a web-based shiny application called the 'PAR(1) Model Analysis'- that allows the modelling, estimation and prediction of a periodic autoregressive time series with scale mixtures of skew-normal innovations, a general and quite flexible class of error distributions-is presented. The class of scale mixtures of skew-normal distributions is often used for statistical procedures of analysing symmetrical and asymmetrical data. The formulation of the scale of a mixture of skew-normal periodic autoregressive models and the estimating methods, which will be applied in the web application, is briefly explained. The embedded tools in the web application and their applications on data analysis are fully described, and finally, a real data example is completely analysed by using this app to illustrate its handling.
ISSN:0094-9655
1563-5163
DOI:10.1080/00949655.2021.2021528