Nothing Special   »   [go: up one dir, main page]

Pagano, 1978 - Google Patents

On periodic and multiple autoregressions

Pagano, 1978

View PDF
Document ID
8177879691492018442
Author
Pagano M
Publication year
Publication venue
The Annals of Statistics

External Links

Snippet

A methodology is presented for analyzing periodic autoregressions which is also applicable when inferring the second order properties of periodically correlated processes. In addition, capitalizing on the connection between periodic and multiple autoregressions, a method is …
Continue reading at projecteuclid.org (PDF) (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10Complex mathematical operations
    • G06F17/14Fourier, Walsh or analogous domain transformations, e.g. Laplace, Hilbert, Karhunen-Loeve, transforms
    • G06F17/141Discrete Fourier transforms

Similar Documents

Publication Publication Date Title
Pagano On periodic and multiple autoregressions
Sauer et al. How many delay coordinates do you need?
Lindgren Some properties of a normal process near a local maximum
DE69619284T3 (en) Device for expanding the voice bandwidth
DE69230308T2 (en) Transformation processing apparatus and method and medium for storing compressed digital data
Isabelle et al. Statistical analysis and spectral estimation techniques for one-dimensional chaotic signals
Kanjilal et al. On the application of orthogonal transformation for the design and analysis of feedforward networks
Gold et al. Analysis of digital and analog formant synthesizers
Yao et al. The generalized Gabor transform
CA2339127C (en) Time-series prediction method and apparatus utilizing wavelet coefficient series
DE4328752B4 (en) Voice recognition system
Gosse et al. Perfect reconstruction versus MMSE filter banks in source coding
Feichtinger et al. Approximate dual Gabor atoms via the adjoint lattice method
Cichocki et al. Simplified neural networks for solving linear least squares and total least squares problems in real time
Shum et al. Speech processing with Walsh-Hadamard transforms
Turchetti et al. On the approximation of stochastic processes by approximate identity neural networks
Shih et al. Recursive soft morphological filters
DE69307192T2 (en) Method and device for concealing transmission errors of digital audio signals coded by means of a frequency transformation
Kirac et al. Results on lattice vector quantization with dithering
Paulin A method for constructing data-based models of spiking neurons using a dynamic linear-static nonlinear cascade
Lindt et al. Discrete denoising flows
Dillon et al. Neural net nonlinear prediction for speech data
Bercher et al. Optimization of bias-variance trade-off in non parametric spectral analysis by decomposition into wavelet packets
HUHNS OPTIMUM RESTORATION OF QUANTIZED CORRELATED SIGNALS.
Yokota et al. A method for estimating coding gain of an orthogonal wavelet transform considering higher‐order statistics