Rate of convergence for a class of RCA estimators

Pavel Vaněček

Kybernetika (2006)

  • Volume: 42, Issue: 6, page 699-709
  • ISSN: 0023-5954

Abstract

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This work deals with Random Coefficient Autoregressive models where the error process is a martingale difference sequence. A class of estimators of unknown parameter is employed. This class was originally proposed by Schick and it covers both least squares estimator and maximum likelihood estimator for instance. Asymptotic behavior of such estimators is explored, especially the rate of convergence to normal distribution is established.

How to cite

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Vaněček, Pavel. "Rate of convergence for a class of RCA estimators." Kybernetika 42.6 (2006): 699-709. <http://eudml.org/doc/33833>.

@article{Vaněček2006,
abstract = {This work deals with Random Coefficient Autoregressive models where the error process is a martingale difference sequence. A class of estimators of unknown parameter is employed. This class was originally proposed by Schick and it covers both least squares estimator and maximum likelihood estimator for instance. Asymptotic behavior of such estimators is explored, especially the rate of convergence to normal distribution is established.},
author = {Vaněček, Pavel},
journal = {Kybernetika},
keywords = {RCA; parameter estimation; rate of convergence; RCA; parameter estimation; rate of convergence},
language = {eng},
number = {6},
pages = {699-709},
publisher = {Institute of Information Theory and Automation AS CR},
title = {Rate of convergence for a class of RCA estimators},
url = {http://eudml.org/doc/33833},
volume = {42},
year = {2006},
}

TY - JOUR
AU - Vaněček, Pavel
TI - Rate of convergence for a class of RCA estimators
JO - Kybernetika
PY - 2006
PB - Institute of Information Theory and Automation AS CR
VL - 42
IS - 6
SP - 699
EP - 709
AB - This work deals with Random Coefficient Autoregressive models where the error process is a martingale difference sequence. A class of estimators of unknown parameter is employed. This class was originally proposed by Schick and it covers both least squares estimator and maximum likelihood estimator for instance. Asymptotic behavior of such estimators is explored, especially the rate of convergence to normal distribution is established.
LA - eng
KW - RCA; parameter estimation; rate of convergence; RCA; parameter estimation; rate of convergence
UR - http://eudml.org/doc/33833
ER -

References

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  7. Nicholls D. F., Quinn B. G., 10.1007/978-1-4684-6273-9, (Lecture Notes in Statistics 11.) Springer, New York 1982 Zbl0497.62081MR0671255DOI10.1007/978-1-4684-6273-9
  8. Phillips P. C. B., Solo V., 10.1214/aos/1176348666, Ann. Statist. 20 (1992), 971–1001 (1992) Zbl0759.60021MR1165602DOI10.1214/aos/1176348666
  9. Schick A., 10.1111/j.1467-842X.1996.tb00671.x, Austral. J. Statist. 38 (1996), 155–160 (1996) MR1442543DOI10.1111/j.1467-842X.1996.tb00671.x
  10. Vaněček P., Estimators of generalized RCA models, In: Proc. WDS’04 Part I (2004), pp. 35–40 

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