The Bayes approach in multiple autoregressive series
Jiří Anděl (1971)
Aplikace matematiky
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Jiří Anděl (1971)
Aplikace matematiky
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Jiří Anděl (1983)
Aplikace matematiky
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Methods for estimating parameters and testing hypotheses in a periodic autoregression are investigated in the paper. The parameters of the model are supposed to be random variables with a vague prior density. The innovation process can have either constant or periodically changing variances. Theoretical results are demonstrated on two simulated series and on two sets of real data.
Jiří Anděl (1987)
Aplikace matematiky
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The model of periodic autoregression is generalized to the multivariate case. The autoregressive matrices are periodic functions of time. The mean value of the process can be a non-vanishing periodic sequence of vectors. Estimators of parameters and tests of statistical hypotheses are based on the Bayes approach. Two main versions of the model are investigated, one with constant variance matrices and the other with periodic variance matrices of the innovation process.
Lubomír Kubáček (1983)
Mathematica Slovaca
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