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高斯自回归移动平均模型大样本极大似然估计量的直接表示AI 解读

SUMMARY A simple procedure is described for obtaining the estimator of the parameters of an autoregressive-moving average process. The estimator is derived from the maximum likelihood procedure assuming Gaussian residuals by making two simplifying assumptions which are negligible in large samples. Components of the estimator are expressed as linear combinations of a suitable set of sample serial correlations or as a rational function of such linear combinations.

高斯 ARMA 过程最大似然估计量的协方差矩阵计算AI 解读

We present a simple procedure for deriving the covariance matrix for the maximum likelihood estimator of a Gaussian autoregressive-moving average process. An illustration of the procedure is given.

高斯移动平均过程参数的最大似然估计AI 解读

Several authors have described methods for computing the exact maximum likelihood estimates for the parameters of a Gaussian moving average process. In this paper, an iterative procedure for solving the likelihood equations is derived which includes the covariance determinant. The procedure expresses the estimator of each parameter as a linear combination of a suitably large set of sample serial correlations which has many of the computational advantages of the direct…

高斯移动平均过程参数的最大似然估计AI 解读

Several authors have described methods for computing the exact maximum likelihood estimates for the parameters of a Gaussian moving average process. In this paper, an iterative procedure for solving the likelihood equations is derived which includes the covariance determinant. The procedure expresses the estimator of each parameter as a linear combination of a suitably large set of sample serial correlations which has many of the computational advantages of the direct…

论贝叶斯稳态预测模型AI 解读

Summary A recent criterion of Smith (1979) seeks to generalize the steady forecasting model of Harrison and Stevens (1976) to take account of non-normality in the observation and system variables. In this note, predictive distributions and forecasts corresponding to quadratic and step loss functions are examined when the observation variable has a distribution belonging to the exponential family. It is shown that Smith's generalized model does not always yield forecasts…

检验自回归移动平均过程阶数的一种方法AI 解读

A procedure is suggested for testing the order of an autoregressive-moving average process which is based on the same set of residual serial correlations as the Box-Pierce test. However, the present method has advantages in terms of its sensitivity to discriminating between alternative models.

基于 Kalman 滤波的多项式投影预测模型的结构表示AI 解读

Summary Harrison and Stevens (1976) give a general class of dynamic linear models which incorporates a stochastic structure for describing model components and which utilizes the Kalman filter algorithm to update the system. This paper derives conditions for the dynamic linear models to yield polynomial-projecting predictors which do not have limiting constraints imposed upon them. Some examples of structurally simple models which satisfy these conditions are given, and the…

高斯移动平均过程参数最大似然估计的不变性性质AI 解读

It is shown that the estimation procedure of Walker leads to estimates of the parameters of a Gaussian moving average process which are asymptotically equivalent to the maximum likelihood estimates proposed by Whittle and represented by Godolphin.

自回归移动平均过程阶数的检验方法AI 解读

SUMMARY A procedure is suggested for testing the order of an autoregressive-moving average process which is based on the same set of residual serial correlations as the Box-Pierce test. However, the present method has advantages in terms of its sensitivity to discriminating between alternative models.

检测移动平均模型系数空缺的大样本检验AI 解读

Summary A procedure is given in the paper for testing the hypothesis that a specified set of parameters in a moving average process assumes the value zero. In particular, the method is applicable for detecting whether a moving average process is seasonal or not. This large-sample test, which operates entirely in the time domain, is based on the estimated asymptotic covariance matrix of improved estimates of the autocorrelation function and is derived from the results of…

利用伪二次收敛准则修正高斯移动平均模型的最大似然估计AI 解读

A criterion is developed in this note for analysing the convergence properties of the direct representation for the maximum likelihood estimator of a Gaussian moving average. It is shown how this leads to a modified system for the estimator which is pseudoquadratically convergent. An illustration of the approach is given.

基于伪二次收敛准则的高斯移动平均修正极大似然估计AI 解读

A criterion is developed in this note for analysing the convergence properties of the direct representation for the maximum likelihood estimator of a Gaussian moving average. It is shown how this leads to a modified system for the estimator which is pseudoquadratically convergent. An illustration of the approach is given.

高斯移动平均过程最大似然估计量的直接表示AI 解读

It has been known for several years that the maTin-mm likelihood estimator of the parameters of a discrete Gaussian moving average process is efficient and asymptotically normally distributed. A procedure is described in this paper which gives an expression for the maximum likelihood estimator of each parameter as a linear combination of a suitable large set of sample serial correlations. An illustration of this procedure is given.

高斯移动平均过程最大似然估计量的直接表示AI 解读

It has been known for several years that the maTin-mm likelihood estimator of the parameters of a discrete Gaussian moving average process is efficient and asymptotically normally distributed. A procedure is described in this paper which gives an expression for the maximum likelihood estimator of each parameter as a linear combination of a suitable large set of sample serial correlations. An illustration of this procedure is given.

基于相关图大样本估计的季节性移动平均模型估计程序AI 解读

Summary In recent years considerable interest has centred on a family of moving average models proposed by Box and Jenkins (1970) for the description of time series which contain a seasonal component. A method is given in the paper for estimating the parameters of the seasonal models based on a principle due to Walker (1961) for the estimation of non-zero members of the autocorrelation function. An illustration of the method is given.

ARIMA过程预测量的一种直接基本形式AI 解读

A direct method of formulating predictors of autoregressive integrated moving average processes is given which has certain advantages over the three basic forms proposed by Box & Jenkins (1970). Some ways of making use of this representation are discussed.

ARIMA过程预测器的一种直接基本形式AI 解读

A direct method of formulating predictors of autoregressive integrated moving average processes is given which has certain advantages over the three basic forms proposed by Box & Jenkins (1970). Some ways of making use of this representation are discussed.