Wavelet Fisher’s Information Measure of 1=fα Signals
This article defines the concept of wavelet-based Fisher’s information measure (wavelet FIM) and develops a closed-form expression of this measure for 1/fα signals. Wavelet Fisher’s information measure characterizes the complexities associated to 1/fα signals and provides a powerful tool for their a...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2011 |
| País: | México |
| Institución: | Instituto Tecnológico y de Estudios Superiores de Occidente |
| Repositorio: | Repositorio Institucional del ITESO |
| Idioma: | inglés |
| OAI Identifier: | oai:rei.iteso.mx:11117/2915 |
| Acceso en línea: | http://hdl.handle.net/11117/2915 |
| Access Level: | acceso abierto |
| Palabra clave: | Structural Breaks Fisher Information Fractal Index Estimation Fractional Gaussian Noise |
| Sumario: | This article defines the concept of wavelet-based Fisher’s information measure (wavelet FIM) and develops a closed-form expression of this measure for 1/fα signals. Wavelet Fisher’s information measure characterizes the complexities associated to 1/fα signals and provides a powerful tool for their analysis. Theoretical and experimental studies demonstrate that this quantity is exponentially increasing for α > 1 (non-stationary signals) and almost constant for α < 1 (stationary signals). Potential applications of wavelet FIM are discussed in some detail and its power and robustness for the detection of structural breaks in the mean embedded in stationary fractional Gaussian noise signals studied. |
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