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1
Artykuł dostępny w postaci pełnego tekstu - kliknij by otworzyć plik
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Testing on the first-order autoregressive model with contaminated exponential white noise finite sample case

100%
Fellag H.
Discussiones Mathematicae Probability and Statistics
|
2001
|
tom 21
|
nr 1
11-20
EN
The testing problem on the first-order autoregressive parameter in finite sample case is considered. The innovations are distributed according to the exponential distribution. The aim of this paper is to study how much the size of this test changes when, at some time k, an innovation outlier contaminant occurs. We show that the test is rather sensitive to these changes.
2
Artykuł dostępny w postaci pełnego tekstu - kliknij by otworzyć plik
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Unit root test in the presence of a single additive outlier small sample case

100%
Fellag H.
Discussiones Mathematicae Probability and Statistics
|
2001
|
tom 21
|
nr 2
89-97
EN
The one sided unit root test of a first-order autoregressive model in the presence of an additive outlier is considered. In this paper, we present a formula to compute the size and the power of the test when an AO (additive outlier) occurs at a time k. A small sample case is considered only.
3
Content available remote

Hurwicz's estimator of the autoregressive model with non-normal innovations

64%
Berkoun Y., Fellag H.
Applicationes Mathematicae
|
2011
|
tom 38
|
nr 2
211-218
EN
Using the Bahadur representation of a sample quantile for m-dependent and strong mixing random variables, we establish the asymptotic distribution of the Hurwicz estimator for the coefficient of autoregression in a linear process with innovations belonging to the domain of attraction of an α-stable law (1 < α < 2). The present paper extends Hurwicz's result to the autoregressive model.
4
Artykuł dostępny w postaci pełnego tekstu - kliknij by otworzyć plik
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Approximate bias for first-order autoregressive model with uniform innovations. Small sample case

64%
Nouali K., Fellag H.
Discussiones Mathematicae Probability and Statistics
|
2002
|
tom 22
|
nr 1-2
15-26
EN
The first-order autoregressive model with uniform innovations is considered. The approximate bias of the maximum likelihood estimator (MLE) of the parameter is obtained. Also, a formula for the approximate bias is given when a single outlier occurs at a specified time with a known amplitude. Simulation procedures confirm that our formulas are suitable. A small sample case is considered only.
5
Artykuł dostępny w postaci pełnego tekstu - kliknij by otworzyć plik
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Bayesian estimation of AR(1) models with uniform innovations

64%
Fellag H., Nouali K.
Discussiones Mathematicae Probability and Statistics
|
2005
|
tom 25
|
nr 1
71-75
EN
The first-order autoregressive model with uniform innovations is considered. In this paper, we propose a family of BAYES estimators based on a class of prior distributions. We obtain estimators of the parameter which perform better than the maximum likelihood estimator.
6
Artykuł dostępny w postaci pełnego tekstu - kliknij by otworzyć plik
Content available

Unit root test in the presence of a single additive outlier small sample case

64%
Fellag H., Abdouche S.
Discussiones Mathematicae Probability and Statistics
|
2002
|
tom 22
|
nr 1-2
5-13
EN
The one sided unit root test of a first-order autoregressive model in the presence of an additive outlier is considered. In this paper, we present a formula to compute the size and the power of the test when an AO (additive outlier) occurs at a time k. A small sample case is considered only.
7
Artykuł dostępny w postaci pełnego tekstu - kliknij by otworzyć plik
Content available

Unit root test under innovation outlier contamination small sample case

51%
Atil L., Fellag H., Nouali K.
Discussiones Mathematicae Probability and Statistics
|
2006
|
tom 26
|
nr 1
5-17
EN
The two sided unit root test of a first-order autoregressive model in the presence of an innovation outlier is considered. In this paper, we present three tests; two are usual and one is new. We give formulas computing the size and the power of the three tests when an innovation outlier (IO) occurs at a specified time, say k. Using a comparative study, we show that the new statistic performs better under contamination. A Small sample case is considered only.
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