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样本时空自协方差与自相关函数的矩AI 解读

Pfeifer & Deutsch (1981) have derived expressions for the means and variances of the sampled autocovariances and autocorrelations, in the case of a white-noise space-time process. This note shows how their work can be extended to general space-time autoregressive moving average processes. The results are useful for testing the significance of observed space-time autocorrelations, given a hypothesized space-time process.

一般移动平均过程自协方差矩阵的逆AI 解读

In this paper we show how the inverse for the general k th autocovariance matrix, for any r th order moving average process, can be obtained by a method which requires inverting no matrix larger than r × r . The method depends on knowing that the inverse of a certain approximating matrix ia just the k th autocovariance matrix for an r th order autoregressive process; and this result is first established. Then, generalizing an approach by Prabhakar Murthy, this inverse is…

一般移动平均过程自协方差矩阵的逆AI 解读

In this paper we show how the inverse for the general k th autocovariance matrix, for any r th order moving average process, can be obtained by a method which requires inverting no matrix larger than r × r . The method depends on knowing that the inverse of a certain approximating matrix ia just the k th autocovariance matrix for an r th order autoregressive process; and this result is first established. Then, generalizing an approach by Prabhakar Murthy, this inverse is…

移动平均过程自相关之和的界AI 解读

Limits for the sums of the complete set of autocorrelations and their absolute values are given for the moving average process.

混合自回归移动平均过程平稳性与可逆性参数约束的递归结构AI 解读

A necessary and sufficient condition for a Schur polynomial is used to develop a method for obtaining the sets of inequalities which define the boundaries of the stationary autoregressive and invertible moving average parameter domains, given any mixed process of order (p, q).

移动平均过程自相关及其绝对值之和的界AI 解读

Limits for the sums of the complete set of autocorrelations and their absolute values are given for the moving average process.

混合 ARMA 过程参数平稳性与可逆性约束的递归性质AI 解读

A necessary and sufficient condition for a Schur polynomial is used to develop a method for obtaining the sets of inequalities which define the boundaries of the stationary autoregressive and invertible moving average parameter domains, given any mixed process of order (p, q).