Edward R. Beadle

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—An algorithm to randomly generate the parameters of stable invertible autoregressive moving average processes of order (p; q)—ARMA (p; q)—is presented. The AR and MA portions are independent of each other, and their respective parameters have jointly uniform distributions with support defined by stability and invertibility considerations. The uniform(More)
It is proposed to jointly estimate the parameters of non-Gaussian autoregressive (AR) processes in a Bayesian context using the Gibbs sampler. Using the Markov chains produced by the sampler an approximation to the vector MAP estimator is implemented. The results reported here used AR(4) models driven by noise sequences where each sample is iid as a two(More)
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