Wavelet Analysis of Generalized Fractional Process

A generalized fractional process is a fairly general model of long-memory, applicable in modeling many random signals whose autocorrelations exhibit hyperbolic and periodic decay. In this paper, we derive a wavelet-based weighted least squares estimator of the long-memory parameter that is relativel...

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Bibliographic Details
Published in2005 5th International Conference on Information Communications and Signal Processing pp. 69 - 72
Main Authors Gonzaga, A., Kawanaka, A.
Format Conference Proceeding
LanguageEnglish
Published IEEE 2005
Subjects
Autocorrelation
Autoregressive processes
Frequency
Generalized fractional process
Heart rate
Least squares approximation
Long-memory
Mathematical model
Maximum likelihood estimation
Pediatrics
Wavelet analysis
Wavelet coefficients
White noise
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ISBN9780780392830
0780392833
DOI10.1109/ICICS.2005.1689006

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Summary:A generalized fractional process is a fairly general model of long-memory, applicable in modeling many random signals whose autocorrelations exhibit hyperbolic and periodic decay. In this paper, we derive a wavelet-based weighted least squares estimator of the long-memory parameter that is relatively efficient. Results show that the proposed method is relatively computationally and statistically efficient. Moreover it allows for estimation of the long-memory parameter without knowledge of the short-memory parameters, which can be estimated using standard methods. We illustrate our approach by an example applying ECG heart rate data
ISBN:9780780392830
0780392833
DOI:10.1109/ICICS.2005.1689006