Robustness of estimation of first-order autoregressive model under contaminated uniform white noise

Karima Nouali

Discussiones Mathematicae Probability and Statistics (2009)

  • Volume: 29, Issue: 1, page 53-68
  • ISSN: 1509-9423

Abstract

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The first-order autoregressive model with uniform innovations is considered. In this paper, we study the bias-robustness and MSE-robustness of modified maximum likelihood estimator of parameter of the model against departures from distribution of white noise. We used the generalized Beta distribution to describe these departures.

How to cite

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Karima Nouali. "Robustness of estimation of first-order autoregressive model under contaminated uniform white noise." Discussiones Mathematicae Probability and Statistics 29.1 (2009): 53-68. <http://eudml.org/doc/277017>.

@article{KarimaNouali2009,
abstract = {The first-order autoregressive model with uniform innovations is considered. In this paper, we study the bias-robustness and MSE-robustness of modified maximum likelihood estimator of parameter of the model against departures from distribution of white noise. We used the generalized Beta distribution to describe these departures.},
author = {Karima Nouali},
journal = {Discussiones Mathematicae Probability and Statistics},
keywords = {autoregressive model; bias; MSE; robustness; generalized Beta distribution; generalized beta distribution; tables},
language = {eng},
number = {1},
pages = {53-68},
title = {Robustness of estimation of first-order autoregressive model under contaminated uniform white noise},
url = {http://eudml.org/doc/277017},
volume = {29},
year = {2009},
}

TY - JOUR
AU - Karima Nouali
TI - Robustness of estimation of first-order autoregressive model under contaminated uniform white noise
JO - Discussiones Mathematicae Probability and Statistics
PY - 2009
VL - 29
IS - 1
SP - 53
EP - 68
AB - The first-order autoregressive model with uniform innovations is considered. In this paper, we study the bias-robustness and MSE-robustness of modified maximum likelihood estimator of parameter of the model against departures from distribution of white noise. We used the generalized Beta distribution to describe these departures.
LA - eng
KW - autoregressive model; bias; MSE; robustness; generalized Beta distribution; generalized beta distribution; tables
UR - http://eudml.org/doc/277017
ER -

References

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  1. [1] J. Andĕl, On AR(1) processes with exponential white noise, Communication In Statistics, A, Theory And Methods 5 (17) (1988), 1481-1495. Zbl0639.62082
  2. [2] C.B. Bell and E.P. Smith, Inference for non-negative autoregressive schemes, Communication In statistics, Theory And Methods 15 (1986), 2267-2293. Zbl0604.62087
  3. [3] H. Fellag and M. Ibazizen, Estimation of the first-order autoregressive model with contaminated exponential white noise, Journal of Mathematical Sciences 106 (1) (2001), 2652-2656. Zbl1002.62067
  4. [4] K. Nouali and H. Fellag, Approximate bias for first-order autoregressive model with uniform innovations. Small sample case, Discussiones Mathematicae, Probability and Statistics 22 (2002), 15-26. Zbl1037.62090
  5. [5] K. Nouali and H. Fellag, Testing on the first-order autoregressive model with uniform innovations under contamination, Journal of Mathematical Sciences 131 (3) (2005), 5657-5663. Zbl06404768
  6. [6] R. Zieliński, Robustness: A quantitative approach, Bulletin de l'Académie Polonaise des Sciences, Serie Sciences Math., Astronomie et Physique XXV (12) (1977), 1281-1286. Zbl0375.62047

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