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多元自回归移动平均过程的拟合AI 解读

Akaike (1974) has shown that any autoregressive-moving average process admits a Markovian representation and conversely. There is a one-to-one correspondence between each autoregressive-moving average process and a member of a class of ‘special’ Markovian representations. Here we shall associate each of the above special representations with a uniquely defined ‘quasiautoregressive-moving average’ representation and we shall estimate the parameters involved by a technique…

多元自回归移动平均过程的拟合AI 解读

Akaike (1974) has shown that any autoregressive-moving average process admits a Markovian representation and conversely. There is a one-to-one correspondence between each autoregressive-moving average process and a member of a class of ‘special’ Markovian representations. Here we shall associate each of the above special representations with a uniquely defined ‘quasiautoregressive-moving average’ representation and we shall estimate the parameters involved by a technique…

有理谱密度连续时间高斯平稳过程的参数估计AI 解读

The estimation of parameters in a continuous time Gaussian stationary process with zero mean and rational spectral density is achieved by an adaptation of the maximum likelihood method. It consists of minimizing something analogous to the sum of the squares of the residuals in the discrete case. The estimate is shown to be asymptotically normal and efficient. A simple computational procedure is also given to construct the estimate.