Recent Trends in Random Data Analysis
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概要
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Recent trends in random data analysis with parametric and nonparametric approaches are reviewed. First, for nonparametric spectrum analysis, the effectiveness of using data windows is emphasized with an example. Secondly, based on a recursive algorithm by Levinson and Durbin, various autoregressive (AR) spectrum estimation techniques by Yule-Walker, Burg and others are explained together with their specific properties. Also, selection criteria for the order of AR model fitting are briefly discussed. As for autoregressive moving average (ARMA) spectrum estimation, the high order Yule-Walker method and its variants are mentioned. Finally, a periodic AR model is introduced for treating random data with periodic structures. An example of applying this model to real temperature data is also given.
- 一般社団法人日本機械学会の論文
著者
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Tokumaru H
Department Of Applied Mathematics And Physics Faculty Of Engineering Kyoto University
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Sakai Hideaki
Department Of Applied Mathematics And Physics Faculty Of Engineering Kyoto University
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TOKUMARU Hidekatsu
Department of Applied Mathematics and Physics, Faculty of Engineering, Kyoto University
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