Title of article :
Classical and Bayesian Estimation of the AR(1) Model with Skew-Symmetric Innovations
Author/Authors :
Hajrajabi, Arezo Department of Statistics - Faculty of Basic Sciences - Imam Khomeini International University, Qazvin, Iran , Fallah, Afshin Department of Statistics - Faculty of Basic Sciences - Imam Khomeini International University, Qazvin, Iran
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 com-
puting 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 eciency and applicability of the proposed
model are assessed via a simulation study and a real-world example.
Keywords :
Skew-normal innovations , Maximum like- lihood estimator , EM algorithm , Bayesian inference , Autoregressive model
