的经典和贝叶斯估计‎ ‎具有斜对称创新的AR（1）模型

IF 0.1 Q4 STATISTICS & PROBABILITY

JIRSS-Journal of the Iranian Statistical Society Pub Date : 2019-06-01 DOI:10.29252/JIRSS.18.1.157

A. Hajrajabi, A. Fallah

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引用次数: 1

摘要

本文从参数的角度考虑了一个具有偏斜正态创新的一阶自回归模型。我们开发了一个基本理论，用于通过期望最大化（EM）算法计算模型参数的最大似然估计。此外，还提出了一种贝叶斯方法来估计模型的未知参数。通过模拟研究和真实世界的例子评估了所提出的模型的效率和适用性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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

. 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 e ﬃ ciency and applicability of the proposed model are assessed via a simulation study and a real-world example.

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来源期刊

JIRSS-Journal of the Iranian Statistical Society STATISTICS & PROBABILITY-

CiteScore

1.50

自引率

0.00%

发文量