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頁籤選單縮合
題 名 | A Nonlinear Smoothing Method for Time Series Analysis |
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作 者 | Kitagawa,Genshiro; | 書刊名 | Statistica Sinica |
卷 期 | 1:2 1991.07[民80.07] |
頁 次 | 頁371-388 |
分類號 | 319.712 |
關鍵詞 | 非線性光滑方法; 時間序列分析; 非線性模式; 非高斯分布; Filtering; Smoothing; Likelihood; AIC; Nonlinear model; Non-Gaussian distribution; |
語 文 | 英文(English) |
英文摘要 | A nonlinear state space approach to the smoothing of time series is shown. The time series is expressed in state space model form where the system model or the observation model contains nonlinear functions of the state vector. Recursive formulas of prediction, filtering and smoothing for the nonlinear state space model are given. Numerical implementation of the formula is shown based on numerical approximation to the densities and numerical computation for the nonlinear transformation of variables, convolution of two densities, Bayes formula, and normalization. Significant merits of nonlinear state space modeling and of the proposed smoother are illustrated by two numerical examples. Empirical study on the numerical accuracy was also performed on one of the examples. |
本系統中英文摘要資訊取自各篇刊載內容。