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This book deals with the methods of detection, and identification of outliers in time series data in the frequency domain. It also discusses the method of analysis that would be insensitive to outliers. The author uses some robust regression method to fit sine and cosine coefficients at each Fourier frequency assuming additive outlier (AO) and multiplicative outliers (MO) respectively to obtain discrete Fourier transform for removing outliers from time series data. The parameters of the contaminated series were estimated using the maximum likelihood (ML) method and the statistical properties of the derived estimates were investigated. Two algorithms were proposed for detection and accommodation of aberrant observations in the frequency domain while modified test statistic using a more robust estimate that is resistant to outlier were also developed to test each observation for discordance. A new filtering method of accommodating outliers was also suggested and the performance of various accommodation techniques was determined in respect of the fixed and dynamic models.Real life and simulated data were used to illustrate the techniques.
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This book deals with the methods of detection, and identification of outliers in time series data in the frequency domain. It also discusses the method of analysis that would be insensitive to outliers. The author uses some robust regression method to fit sine and cosine coefficients at each Fourier frequency assuming additive outlier (AO) and multiplicative outliers (MO) respectively to obtain discrete Fourier transform for removing outliers from time series data. The parameters of the contaminated series were estimated using the maximum likelihood (ML) method and the statistical properties of the derived estimates were investigated. Two algorithms were proposed for detection and accommodation of aberrant observations in the frequency domain while modified test statistic using a more robust estimate that is resistant to outlier were also developed to test each observation for discordance. A new filtering method of accommodating outliers was also suggested and the performance of various accommodation techniques was determined in respect of the fixed and dynamic models.Real life and simulated data were used to illustrate the techniques.
Biografía del autor
Dr. O.I. Shittu had B.Sc, M.Sc; M.Phil and Ph.D degrees in statistics from the University of Ibadan, Nigeria. A Chattered Statistician (CStat) and Chattered Scientist (CSci) of the Royal Statistical Society (RSS) London. He also hold an Ordinary Certificate in Statistics awarded by the Institute of Statisticians (IOS) London in 1986
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Analysis of Time Series Data in the Presence of Outliers
Verlag:
LAP LAMBERT Academic Publishing Apr 2011, 2011
ISBN 10: 3844323546
ISBN 13: 9783844323542
Neu
Taschenbuch
Anbieter: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Deutschland
Verkäuferbewertung 5 von 5 Sternen
Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book deals with the methods of detection, and identification of outliers in time series data in the frequency domain. It also discusses the method of analysis that would be insensitive to outliers. The author uses some robust regression method to fit sine and cosine coefficients at each Fourier frequency assuming additive outlier (AO) and multiplicative outliers (MO) respectively to obtain discrete Fourier transform for removing outliers from time series data. The parameters of the contaminated series were estimated using the maximum likelihood (ML) method and the statistical properties of the derived estimates were investigated. Two algorithms were proposed for detection and accommodation of aberrant observations in the frequency domain while modified test statistic using a more robust estimate that is resistant to outlier were also developed to test each observation for discordance. A new filtering method of accommodating outliers was also suggested and the performance of various accommodation techniques was determined in respect of the fixed and dynamic models.Real life and simulated data were used to illustrate the techniques. 132 pp. Englisch. Bestandsnummer des Verkäufers 9783844323542
Anzahl: 2 verfügbar
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Analysis of Time Series Data in the Presence of Outliers
Verlag:
LAP LAMBERT Academic Publishing, 2011
ISBN 10: 3844323546
ISBN 13: 9783844323542
Neu
Softcover
Anbieter: moluna, Greven, Deutschland
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Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Shittu OlanrewajuDr. O.I. Shittu had B.Sc, M.Sc M.Phil and Ph.D degrees in statistics from the University of Ibadan, Nigeria. A Chattered Statistician (CStat) and Chattered Scientist (CSci) of the Royal Statistical Society (RSS) Lon. Bestandsnummer des Verkäufers 5472781
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Analysis of Time Series Data in the Presence of Outliers
Verlag:
LAP LAMBERT Academic Publishing Apr 2011, 2011
ISBN 10: 3844323546
ISBN 13: 9783844323542
Neu
Taschenbuch
Anbieter: buchversandmimpf2000, Emtmannsberg, BAYE, Deutschland
Verkäuferbewertung 5 von 5 Sternen
Taschenbuch. Zustand: Neu. Neuware -This book deals with the methods of detection, and identification of outliers in time series data in the frequency domain. It also discusses the method of analysis that would be insensitive to outliers. The author uses some robust regression method to fit sine and cosine coefficients at each Fourier frequency assuming additive outlier (AO) and multiplicative outliers (MO) respectively to obtain discrete Fourier transform for removing outliers from time series data. The parameters of the contaminated series were estimated using the maximum likelihood (ML) method and the statistical properties of the derived estimates were investigated. Two algorithms were proposed for detection and accommodation of aberrant observations in the frequency domain while modified test statistic using a more robust estimate that is resistant to outlier were also developed to test each observation for discordance. A new filtering method of accommodating outliers was also suggested and the performance of various accommodation techniques was determined in respect of the fixed and dynamic models.Real life and simulated data were used to illustrate the techniques.Books on Demand GmbH, Überseering 33, 22297 Hamburg 132 pp. Englisch. Bestandsnummer des Verkäufers 9783844323542
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Analysis of Time Series Data in the Presence of Outliers : In frequency domain
Verlag:
LAP LAMBERT Academic Publishing, 2011
ISBN 10: 3844323546
ISBN 13: 9783844323542
Neu
Taschenbuch
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Verkäuferbewertung 5 von 5 Sternen
Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book deals with the methods of detection, and identification of outliers in time series data in the frequency domain. It also discusses the method of analysis that would be insensitive to outliers. The author uses some robust regression method to fit sine and cosine coefficients at each Fourier frequency assuming additive outlier (AO) and multiplicative outliers (MO) respectively to obtain discrete Fourier transform for removing outliers from time series data. The parameters of the contaminated series were estimated using the maximum likelihood (ML) method and the statistical properties of the derived estimates were investigated. Two algorithms were proposed for detection and accommodation of aberrant observations in the frequency domain while modified test statistic using a more robust estimate that is resistant to outlier were also developed to test each observation for discordance. A new filtering method of accommodating outliers was also suggested and the performance of various accommodation techniques was determined in respect of the fixed and dynamic models.Real life and simulated data were used to illustrate the techniques. Bestandsnummer des Verkäufers 9783844323542
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Analysis of Time Series Data in the Presence of Outliers: In frequency domain
Verlag:
LAP LAMBERT Academic Publishing, 2011
ISBN 10: 3844323546
ISBN 13: 9783844323542
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Paperback. Zustand: Like New. Like New. book. Bestandsnummer des Verkäufers ERICA79638443235466
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