A probability-density-based autoregressive model using support vector method and higher-order spectra estimation
✍ Scribed by Yoshitomo Nishiguchi; Naohiro Toda; Shiro Usui
- Book ID
- 102822136
- Publisher
- John Wiley and Sons
- Year
- 2006
- Tongue
- English
- Weight
- 565 KB
- Volume
- 89
- Category
- Article
- ISSN
- 1042-0967
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✦ Synopsis
Abstract
When the characteristics of non‐Gaussian time series such as biological signals are described, not only power spectra but also higher‐order spectra are required. In order to obtain estimated values with few statistical fluctuations, some parametric estimation method has to be established. As the parametric model, regression‐function‐based autoregressive models such as the neural network autoregressive model have been studied so far. On the other hand, probability‐density‐based autoregressive models in which the correlation information of the time series is represented by the conditional probability density function have been proposed. However, in the existing probability‐density‐based autoregressive models, higher‐order spectral estimation is not assumed. So, if we adopt the probability‐density‐based autoregressive model for the estimation of higher‐order spectra, some problems such as the stationarity arise. In this paper, we proposed a new probability‐density‐based autoregressive model using the support vector method. Further, we estimated higher‐order spectra of the time series by the proposed model. © 2006 Wiley Periodicals, Inc. Electron Comm Jpn Pt 3, 89(10): 1–10, 2006; Published online in Wiley InterScience (www.interscience. wiley.com). DOI 10.1002/ecjc.20225