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http://dx.doi.org/10.5351/KJAS.2014.27.6.1039

Bayesian Inference for Autoregressive Models with Skewed Exponential Power Errors  

Ryu, Hyunnam (Department of Statistics, Kyungpook National University)
Kim, Dal Ho (Department of Statistics, Kyungpook National University)
Publication Information
The Korean Journal of Applied Statistics / v.27, no.6, 2014 , pp. 1039-1047 More about this Journal
Abstract
An autoregressive model with normal errors is a natural model that attempts to fit time series data. More flexible models that include normal distribution as a special case are necessary because they can cover normality to non-normality models. The skewed exponential power distribution is a possible candidate for autoregressive models errors that may have tails lighter(platykurtic) or heavier(leptokurtic) than normal and skewness; in addition, the use of skewed exponential power distribution can reduce the influence of outliers and consequently increases the robustness of the analysis. We use SIR algorithm and grid method for an efficient Bayesian estimation.
Keywords
Autoregressive model; Bayesian p-value; skewed exponential power distribution; Gibbs sampler; robust;
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