Bayesian estimation of an AR(1) process with exponential white noise

Ibazizen, Mohamed; Fellag, Hocine

doi:10.1080/0233188031000078042

Cited by 19 publications

(9 citation statements)

References 2 publications

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“…Ghosh and Heo (2003) proposed a comparative study using some selected noninformative (objective) priors for the AR(1) model. Ibazizen and Fellag (2003), assumed a noninformative prior for the autoregressive parameter without considering the stationarity assumption for the AR(1) model. However, most papers consider a noninformative (objective) prior for the Bayesian analysis of an AR(1) model without considering the stationarity assumption.…”

Section: The Dynamic Frameworkmentioning

confidence: 99%

Robust Dynamic Panel Data Models Using E-Contamination

et al. 2020

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Section: The Dynamic Frameworkmentioning

confidence: 99%

Robust Dynamic Panel Data Models Using E-Contamination

et al. 2020

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“…It is well known that the ML estimates are less accurate for small sample sizes. In this situation, the Bayesian approach provides an interesting alternative methodology for inference and modeling especially when prior information about the unknown parameters is available (Ibazizen and Fellag , 2003). The Bayesian approach incorporates our prior knowledge about parameters, in terms of the prior distributions, and the information obtained from observations via Bayes rule.…”

Section: Bayesian Approachmentioning

confidence: 99%

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

Hajrajabi¹,

Fallah²

2019

JIRSS

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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.

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“…A smaller error signifies better accuracy. The error of a time-series model, which is called white noise, usually follows a normal distribution, but another form of time-series model with auto-correlated observations is exponential white noise [16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Average Run Length on CUSUM Control Chart for Seasonal and Non-Seasonal Moving Average Processes with Exogenous Variables

Sunthornwat¹,

Areepong

2020

Symmetry

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The aim of this study was to derive explicit formulas of the average run length (ARL) of a cumulative sum (CUSUM) control chart for seasonal and non-seasonal moving average processes with exogenous variables, and then evaluate it against the numerical integral equation (NIE) method. Both methods had similarly excellent agreement, with an absolute percentage error of less than 0.50%. When compared to other methods, the explicit formula method is extremely useful for finding optimal parameters when other methods cannot. In this work, the procedure for obtaining optimal parameters—which are the reference value ( a ) and control limit ( h )—for designing a CUSUM chart with a minimum out-of-control ARL is presented. In addition, the explicit formulas for the CUSUM control chart were applied with the practical data of a stock price from the stock exchange of Thailand, and the resulting performance efficiency is compared with an exponentially weighted moving average (EWMA) control chart. This comparison showed that the CUSUM control chart efficiently detected a small shift size in the process, whereas the EWMA control chart was more efficient for moderate to large shift sizes.

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Bayesian estimation of an AR(1) process with exponential white noise

Cited by 19 publications

References 2 publications

Robust Dynamic Panel Data Models Using E-Contamination

Robust Dynamic Panel Data Models Using E-Contamination

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

Average Run Length on CUSUM Control Chart for Seasonal and Non-Seasonal Moving Average Processes with Exogenous Variables

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