工程数学学报
工程數學學報
공정수학학보
CHINESE JOURNAL OF ENGINEERING MATHEMATICS
2014年
5期
733-744
,共12页
非线性结构向量自回归模型%图模型%条件独立%条件互信息
非線性結構嚮量自迴歸模型%圖模型%條件獨立%條件互信息
비선성결구향량자회귀모형%도모형%조건독립%조건호신식
nonlinear structural vector autoregressive models%graphical models%conditional independence%conditional mutual information
确定变量间的因果关系是时间序列分析的重要内容。传统的图模型因果推断算法有着明显的局限性,要求模型是线性的且噪声项服从Gauss分布。本文利用图模型方法辨识非线性结构向量自回归模型变量间的因果关系,给出了一种基于互信息和条件互信息的非线性结构向量自回归因果图模型结构的非参数辨识方法。数值模拟结果验证了方法的有效性。
確定變量間的因果關繫是時間序列分析的重要內容。傳統的圖模型因果推斷算法有著明顯的跼限性,要求模型是線性的且譟聲項服從Gauss分佈。本文利用圖模型方法辨識非線性結構嚮量自迴歸模型變量間的因果關繫,給齣瞭一種基于互信息和條件互信息的非線性結構嚮量自迴歸因果圖模型結構的非參數辨識方法。數值模擬結果驗證瞭方法的有效性。
학정변량간적인과관계시시간서렬분석적중요내용。전통적도모형인과추단산법유착명현적국한성,요구모형시선성적차조성항복종Gauss분포。본문이용도모형방법변식비선성결구향량자회귀모형변량간적인과관계,급출료일충기우호신식화조건호신식적비선성결구향량자회귀인과도모형결구적비삼수변식방법。수치모의결과험증료방법적유효성。
It is important to detect and clarify the cause-effect relationships among variables in time series analysis. Traditional graphical models causality inference methods have a salient limitation that the model must be linear and with Gaussian noise. In this paper, we apply the graphical models to infer the causal relationships a-mong variables of nonlinear structural vector autoregressive models. We propose a nonparametric method which employs both the mutual information and condi-tional mutual information to identify the causal structure of nonlinear structural vector autoregressive causal graph model. Numerical simulations demonstrate the effectiveness of the method.