Statistical analysis of periodic autoregression

Jiří Anděl

Aplikace matematiky (1983)

  • Volume: 28, Issue: 5, page 364-385
  • ISSN: 0862-7940

Abstract

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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.

How to cite

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Anděl, Jiří. "Statistical analysis of periodic autoregression." Aplikace matematiky 28.5 (1983): 364-385. <http://eudml.org/doc/15316>.

@article{Anděl1983,
abstract = {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.},
author = {Anděl, Jiří},
journal = {Aplikace matematiky},
keywords = {periodic autoregression; vague prior density; innovation process; changing variances; simulated series; real data; periodic autoregression; vague prior density; innovation process; changing variances; simulated series; real data},
language = {eng},
number = {5},
pages = {364-385},
publisher = {Institute of Mathematics, Academy of Sciences of the Czech Republic},
title = {Statistical analysis of periodic autoregression},
url = {http://eudml.org/doc/15316},
volume = {28},
year = {1983},
}

TY - JOUR
AU - Anděl, Jiří
TI - Statistical analysis of periodic autoregression
JO - Aplikace matematiky
PY - 1983
PB - Institute of Mathematics, Academy of Sciences of the Czech Republic
VL - 28
IS - 5
SP - 364
EP - 385
AB - 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.
LA - eng
KW - periodic autoregression; vague prior density; innovation process; changing variances; simulated series; real data; periodic autoregression; vague prior density; innovation process; changing variances; simulated series; real data
UR - http://eudml.org/doc/15316
ER -

References

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  1. J. Anděl, The Statistical Analysis of Time Series, SNTL, Prague 1976 (in Czech). (1976) 
  2. J. Anděl, Mathematical Statistics, SNTL, Prague, 1978 (in Czech). (1978) 
  3. G. E. P. Box G. M. Jenkins, Time Series Analysis, Forecasting and Control, Holden Day, San Francisco, 1970. (1970) MR0272138
  4. G. E. P. Box G. C. Tiao, 10.1080/01621459.1975.10480264, J. Amer. Statist. Assoc. 70 (1975), 70-79. (1975) MR0365957DOI10.1080/01621459.1975.10480264
  5. W. P. Cleveland G. C. Tiao, Modeling seasonal time series, Rev. Economic Appliquée 32 (1979), 107-129. (1979) 
  6. E. G. Gladyshev, Periodically correlated random sequences, Soviet Math. 2 (1961), 385-388. (1961) Zbl0212.21401
  7. E. G. Gladyshev, Periodically and almost periodically correlated random processes with continuous time parameter, Theory Prob. Appl. 8 (1983), 173-177. (1983) 
  8. J. Janko, Statistical Tables, NČSAV, Prague, 1958 (in Czech). (1958) MR0150924
  9. N. L. Johnson S. Kotz, Distributions in Statistics: Continuous Multivariate Distributions, Wiley, New York, 1972. (1972) MR0418337
  10. R. H. Jones W. M. Brelsford, 10.1093/biomet/54.3-4.403, Biometrika 54 (1967), 403-408. (1967) MR0223041DOI10.1093/biomet/54.3-4.403
  11. H. J. Newton, 10.1080/00401706.1982.10487731, Technometrics 24 (1982), 109-116. (1982) MR0655574DOI10.1080/00401706.1982.10487731
  12. M. Pagano, 10.1214/aos/1176344376, Ann. Statist. 6 (1978), 1310-1317. (1978) MR0523765DOI10.1214/aos/1176344376
  13. C. G. Tiao M. R. Grupe, Hidden periodic autoregressive-moving average models in time series data, Biometrika 67 (1980), 365-373. (1980) MR0581732
  14. A. Zellner, An Introduction to Bayesian Inference in Econometrics, Wiley, New York, 1971. (1971) Zbl0246.62098MR0433791

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