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An introduction to wavelets and other filtering methods in finance and economics / Ramazan Gencay, Faruk Selcuk, Brandon Whitcher.

By: Contributor(s): Material type: TextTextLanguage: English Publication details: San Diego : Academic Press, 2002.Description: xxii, 359 páginas : ilustraciones, gráficas, tablas ; 23 cmContent type:
  • Texto
Media type:
  • Sin mediación
Carrier type:
  • Volumen
ISBN:
  • 0122796705
Subject(s): DDC classification:
  • 330.015195 G35i  21
Other classification:
  • B23
Contents:
I. Introduction: 1.1. Fourier versus Wavelet analysis ; 1.2. Seasonality filtering ; 1.3. Denoising ; 1.4. Identification of structural breaks ; 1.5. Scaling ; 1.6. Aggregate heterogeneity and timescales ; 1.7. Multiscale cross-correlation ; 1.8. Outline -- 2. Linear filters: 2.1. Introduction ; 2.2. Filters in time domain ; 2.3. Filters in the frequency domain ; 2.4. Filters in practice -- 3 .Optimum linear estimation: 3.1. Introduction ; 3.2. The wiener filter and estimation ; 3.3. Recursive filtering and the Kalman filter ; 3.4. Prediction with the Kalman filter ; 3.5. Vector Kalmar filter estimation ; 3.6. Application -- 4. Discrete wavelet transforms: 4.1. Introduction ; 4.2. Properties of the wavelet transform ; 4.3. Discrete wavelet filters ; 4.4. The discrete wavelet transform ; 4.5. The maximal overlap discrete wavelet transform ; 4.6. Practical issues in implementation ; 4.7. Applications -- 5. Wavelets and stationary processes: 5.1. Introduction ; 5.2. Wavelets and long-memory processes ; 5.3. Generalizations of the DWT and MODWT ; 5.4. Wavelets and seasonal long memory ; 5.5. Application -- 6. Wavelet Denoising: 6.1. Introduction ; 6.2. Nonlinear Denoising via Thresholding ; 6.3. Thresholds selection ; 6.4. Implementing wavelet Denoising ; 6.5. Application -- 7. Wavelets for variance-covariance estimation: 7.1. Introduction ; 7.2. The wavelet variance ; 7.3. Testing homogeneity of variance ; 7.4. The wavelet covariance and cross-covariance ; 7.5. The wavelet correlation and cross-correlation ; 7.6. Applications ; 7.7. Univariate and bivariate spectrum analysis -- 8. Artificial neural networks: 8.1. Introduction ; 8.2. Activation functions ; 8.3. Feedforward networks ; 8.4. Recurrent networks ; 8.5. Network selection ; 8.5. Network selections ; 8.6. Adaptivity ; 8.7. Estimation of recurrent networks ; 8.8. Applications of neural networks models.
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Holdings
Item type Home library Call number Status Notes Date due Barcode Item holds
LIBRO FISICO Biblioteca Principal 330.015195 G35i (Browse shelf(Opens below)) Pending hold Mantener en colección. 29004018979921 1
Total holds: 1

Incluye referencias bibliográficas (páginas 323-348) e índice.

I. Introduction: 1.1. Fourier versus Wavelet analysis ; 1.2. Seasonality filtering ; 1.3. Denoising ; 1.4. Identification of structural breaks ; 1.5. Scaling ; 1.6. Aggregate heterogeneity and timescales ; 1.7. Multiscale cross-correlation ; 1.8. Outline -- 2. Linear filters: 2.1. Introduction ; 2.2. Filters in time domain ; 2.3. Filters in the frequency domain ; 2.4. Filters in practice -- 3 .Optimum linear estimation: 3.1. Introduction ; 3.2. The wiener filter and estimation ; 3.3. Recursive filtering and the Kalman filter ; 3.4. Prediction with the Kalman filter ; 3.5. Vector Kalmar filter estimation ; 3.6. Application -- 4. Discrete wavelet transforms: 4.1. Introduction ; 4.2. Properties of the wavelet transform ; 4.3. Discrete wavelet filters ; 4.4. The discrete wavelet transform ; 4.5. The maximal overlap discrete wavelet transform ; 4.6. Practical issues in implementation ; 4.7. Applications -- 5. Wavelets and stationary processes: 5.1. Introduction ; 5.2. Wavelets and long-memory processes ; 5.3. Generalizations of the DWT and MODWT ; 5.4. Wavelets and seasonal long memory ; 5.5. Application -- 6. Wavelet Denoising: 6.1. Introduction ; 6.2. Nonlinear Denoising via Thresholding ; 6.3. Thresholds selection ; 6.4. Implementing wavelet Denoising ; 6.5. Application -- 7. Wavelets for variance-covariance estimation: 7.1. Introduction ; 7.2. The wavelet variance ; 7.3. Testing homogeneity of variance ; 7.4. The wavelet covariance and cross-covariance ; 7.5. The wavelet correlation and cross-correlation ; 7.6. Applications ; 7.7. Univariate and bivariate spectrum analysis -- 8. Artificial neural networks: 8.1. Introduction ; 8.2. Activation functions ; 8.3. Feedforward networks ; 8.4. Recurrent networks ; 8.5. Network selection ; 8.5. Network selections ; 8.6. Adaptivity ; 8.7. Estimation of recurrent networks ; 8.8. Applications of neural networks models.

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