頁籤選單縮合
題 名 | Posterior Mode Estimation for Nonlinear and Non-Gaussian State Space Models |
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作 者 | So,Mike K. P.; | 書刊名 | Statistica Sinica |
卷 期 | 13:1 2003.01[民92.01] |
頁 次 | 頁255-274 |
分類號 | 319.712 |
關鍵詞 | Filtering; Kalman filter; Quadratic hill-climbing; Stochastic volatility model; Time series; |
語 文 | 英文(English) |
英文摘要 | In this paper, we develop a posterior mode estimation method for nonlinear and non-Gaussian state space models. By exploiting special structures of the state space models, we derive a modified quadratic hill-climbing procedure which can be implemented efficiently in O(n) operations. The method can be used for estimating the state variable, performing Bayesian inference and carrying out Monte Carlo likelihood inference. Numerical illustrations using simulated and real data demonstrate that our procedure is much more efficient than a common gradient method. It is also evident that our method works very well in a new stochastic volatility model which contains a nonlinear state equation. |
本系統中英文摘要資訊取自各篇刊載內容。