Classical and Bayesian Estimation of the‎ ‎AR(1) Model with Skew-Symmetric Innovations

Authors

IKIU

10.29252/jirss.18.1.157

Abstract

This paper considers a first-order autoregressive model   with skew-normal innovations from a parametric point of view.   We develop an essential theory for computing the maximum likelihood estimation of model parameters via   an Expectation- Maximization (EM) algorithm.  Also, a Bayesian  method  is   proposed to estimate  the unknown parameters of the model.   The efficiency  and applicability  of the proposed model are   assessed  via  a simulation study and a real-world example.
 

Keywords

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Volume 18, Issue 1
June 2019
Pages 157-175
  • Receive Date: 23 July 2022
  • Revise Date: 20 May 2024
  • Accept Date: 23 July 2022