论文检索

时间序列中的异常值检测AI 解读

SUMMARY The estimation and detection of outliers in a time series generated by a Gaussian auto-regressive moving average process is considered. It is shown that the estimation of additive outliers is directly related to the estimation of missing or deleted observations. A recursive procedure for computing the estimates is given. Likelihood ratio and score criteria for detecting additive outliers are examined and are shown to be closely related to the leave-k-out diagnostics…

单变量时间序列模型的诊断检验AI 解读

The portmanteau statistic for testing the adequacy of an autoregressive-moving average model is based on the first m autocorrelations of the residuals from the fitted model. This paper examines the properties of this test for various choices of m. A modification which allows the use of small values of m is shown to result in a more powerful test. The Lagrange multiplier statistic (Godfrey, 1979) and a test statistic examined by Newbold (1980) are also discussed.

含缺失观测的平稳高斯 ARMA 过程的似然函数AI 解读

The likelihood function for an autoregressive-moving average process observed at n equally spaced time points has a well-known form. This note gives an expression for the likelihood function when some observations are missing from the series and shows how the missing observations may be estimated from the available data.

存在缺失观测的平稳高斯 ARMA 过程似然函数AI 解读

The likelihood function for an autoregressive-moving average process observed at n equally spaced time points has a well-known form. This note gives an expression for the likelihood function when some observations are missing from the series and shows how the missing observations may be estimated from the available data.

平稳自回归移动平均模型的似然函数AI 解读

This paper examines the likelihood function of a stationary autoregressive-moving average model of order (p, q) and presents a method for its evaluation. Numerical results illustrating the computational efficiency of the method are given.