On periodic autoregression with unknown mean

Jiří Anděl; Asunción Rubio; Antonio Insua

Aplikace matematiky (1985)

  • Volume: 30, Issue: 2, page 126-139
  • ISSN: 0862-7940

Abstract

top
If the parameters of an autoregressive model are periodic functions we get a periodic autoregression. In the paper the case is investigated when the expectation can also be a periodic function. The innovations have either constant or periodically changing variances.

How to cite

top

Anděl, Jiří, Rubio, Asunción, and Insua, Antonio. "On periodic autoregression with unknown mean." Aplikace matematiky 30.2 (1985): 126-139. <http://eudml.org/doc/15390>.

@article{Anděl1985,
abstract = {If the parameters of an autoregressive model are periodic functions we get a periodic autoregression. In the paper the case is investigated when the expectation can also be a periodic function. The innovations have either constant or periodically changing variances.},
author = {Anděl, Jiří, Rubio, Asunción, Insua, Antonio},
journal = {Aplikace matematiky},
keywords = {estimating parameters; testing hypotheses; Periodic autoregressive models; time-varying coefficients; Gaussian white noise; unknown mean; innovation; seasonal series; Gaussian maximum likelihood methods; estimating parameters; testing hypotheses; Periodic autoregressive models; time-varying coefficients; Gaussian white noise; unknown mean; innovation; seasonal series; Gaussian maximum likelihood methods},
language = {eng},
number = {2},
pages = {126-139},
publisher = {Institute of Mathematics, Academy of Sciences of the Czech Republic},
title = {On periodic autoregression with unknown mean},
url = {http://eudml.org/doc/15390},
volume = {30},
year = {1985},
}

TY - JOUR
AU - Anděl, Jiří
AU - Rubio, Asunción
AU - Insua, Antonio
TI - On periodic autoregression with unknown mean
JO - Aplikace matematiky
PY - 1985
PB - Institute of Mathematics, Academy of Sciences of the Czech Republic
VL - 30
IS - 2
SP - 126
EP - 139
AB - If the parameters of an autoregressive model are periodic functions we get a periodic autoregression. In the paper the case is investigated when the expectation can also be a periodic function. The innovations have either constant or periodically changing variances.
LA - eng
KW - estimating parameters; testing hypotheses; Periodic autoregressive models; time-varying coefficients; Gaussian white noise; unknown mean; innovation; seasonal series; Gaussian maximum likelihood methods; estimating parameters; testing hypotheses; Periodic autoregressive models; time-varying coefficients; Gaussian white noise; unknown mean; innovation; seasonal series; Gaussian maximum likelihood methods
UR - http://eudml.org/doc/15390
ER -

References

top
  1. J. Anděl, Statistical analysis of periodic autoregression, Apl. mat. 28 (1983), 364-365. (1983) MR0712913
  2. G. E. P. Box G. C. Tiao, 10.1080/01621459.1975.10480264, J. Amer. Statist. Assoc. 70 (1975), 70-79. (1975) Zbl0316.62045MR0365957DOI10.1080/01621459.1975.10480264
  3. E. G. Gladyshev, Periodically correlated random sequences, Soviet Math. 2 (1961), 385-388. (1961) Zbl0212.21401
  4. E. G. Gladyshev, Periodically and almost periodically correlated random process with continuous time parameter, Theory Prob. Appl. 8 (1963), 173-177. (1963) Zbl0138.11003
  5. M. Pagano, 10.1214/aos/1176344376, Ann. Statist. 6 (1978), 1310-1317. (1978) Zbl0392.62073MR0523765DOI10.1214/aos/1176344376
  6. A. Zellner, An introduction to Bayesian Inference in Econometrics, Wiley, New York, 1971. (1971) Zbl0246.62098MR0433791

NotesEmbed ?

top

You must be logged in to post comments.

To embed these notes on your page include the following JavaScript code on your page where you want the notes to appear.

Only the controls for the widget will be shown in your chosen language. Notes will be shown in their authored language.

Tells the widget how many notes to show per page. You can cycle through additional notes using the next and previous controls.

    
                

Note: Best practice suggests putting the JavaScript code just before the closing </body> tag.