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未知阶自回归过程系数最小二乘估计量的矩收敛AI 解读

Given a realization of $T$ consecutive observations of a stationary autoregressive process of unknown, possibly infinite, order $m$, it is assumed that a process of arbitrary finite order $p$ is fitted by least squares. Under appropriate conditions it is known that the estimators of the autoregressive coefficients are asymptotically normal. The question considered here is whether the moments of the (scaled) estimators converge, as $T \rightarrow \infty$, to the moments of…

无限方差过程的一致阶数判定AI 解读

SUMMARY Order determination for autoregressive processes when the residuals have a distribution belonging to the domain of attraction of an infinite variance stable law is examined. The use of the FPEα criterion of Bhansali and Downham (1977) is shown to provide a consistent estimator of the order with any α > 0. Order determination for moving average processes is considered; a procedure based on autoregressive estimates of the inverse correlations, see Bhansali (1983), is…

插值误差方差与线性确定性指标的估计AI 解读

The autoregressive and window estimators of the inverse variance, Ri (0), say, are adopted for estimating the interpolation error variance, {Ri (0)}−1, and an index of linear determinism, A, of a stationary process {Xt}. The index A is defined as 1 −{R(0) Ri (0)}−1, where R(0) denotes the variance of Xt; A measures the proportion of total variance explained by the interpolator when interpolating Xt, say, from {Xs, S ≠ t}. Some uses of A and {Ri (0)}−1 are discussed. The…

插值误差方差与线性确定性指标的估计AI 解读

The autoregressive and window estimators of the inverse variance, Ri (0), say, are adopted for estimating the interpolation error variance, {Ri (0)}−1, and an index of linear determinism, A, of a stationary process {Xt}. The index A is defined as 1 −{R(0) Ri (0)}−1, where R(0) denotes the variance of Xt; A measures the proportion of total variance explained by the interpolator when interpolating Xt, say, from {Xs, S ≠ t}. Some uses of A and {Ri (0)}−1 are discussed. The…

自回归传递函数准则选阶的渐近效率AI 解读

The autoregressive orders selected by the criterion autoregressive transfer function (CAT) of Parzen (1974), a new version, CAT$^\ast$, of CAT introduced by Parzen (1977), and the CAT$_2$ criterion of Bhansali (1985) are shown to be asymptotically efficient in the sense defined by Shibata (1980, 1981). A generalization of the penalty function considered by Shibata (1980) is introduced. The order selected by the CAT$_\alpha$ criterion of Bhansali (1985), with any fixed $\alpha > 1$…

自回归模型拟合中若干二次型的计算AI 解读

Let $\mathbf{R}$ be an infinite dimensional stationary covariance matrix, let $\mathbf{R}(k)$ and $\mathbf{W}(k)$ denote the top $k \times k$ left hand corners of $\mathbf{R}$ and $\mathbf{R}^{-1}$ respectively and let $\mathbf{\Sigma}(k)$ and $\mathbf{\Gamma}(k)$ denote the approximations for $\mathbf{R}(k)^{-1}$ suggested by Whittle (1951) and Shaman (1976) respectively. We consider quadratic forms of the type $Q(k) = \beta(k)' \mathbf{R}(k)^{-1}\alpha (k)$, when the…

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未知自回归阶数对预测均方误差的影响（一）AI 解读

Given a realization of T consecutive observations of an autoregressive process, x, of unknown order m, a process of an arbitrary order p is assumed to be fitted for predicting the future values of another process [xtilde](t), where [xtilde](t) is independent of xt but otherwise has identical probabilistic structure. The mean squared error of prediction when the autoregressive coefficients are estimated by least squares is obtained to terms of order T -1. The bias in…

逆相关函数的自回归与窗口估计AI 解读

The concept of the inverse correlation function of a stationary process xt was first introduced by Cleveland (1972), who also introduced the autoregressive and the window methods for estimating this function. The asymptotic distribution of the estimates provided by these two methods is derived and their asymptotic covariance structure is shown to be in accordance with a remark of Parzen (1974). The results are extended to show that the two procedures suggested by Durbin…

逆相关函数的自回归与窗估计AI 解读

The concept of the inverse correlation function of a stationary process xt was first introduced by Cleveland (1972), who also introduced the autoregressive and the window methods for estimating this function. The asymptotic distribution of the estimates provided by these two methods is derived and their asymptotic covariance structure is shown to be in accordance with a remark of Parzen (1974). The results are extended to show that the two procedures suggested by Durbin…

通过时域自回归模型拟合进行线性预测AI 解读

Let $\{x_t\}$ be a purely nondeterministic stationary process satisfying all the assumptions made by Berk (1974), and $\{y_t\}$ be another purely nondeterministic stationary process. Assume that $y_t$ is independent of $x_t$ but has exactly the same statistical properties as that of $x_t$. Consider the linear prediction of future values of $y_t$ on the basis of past values, using prediction constants estimated from a realisation of $T$ observations of $x_t$ by least-squares…

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Wiener–Kolmogorov 预测器的渐近性质（二）AI 解读

Summary The asymptotic properties of the autoregressive estimates a (T)(u) obtained using the fles method (Bhansali, 1974) suggest that, in practice, a decision on truncating the a (T)(u) at u = p, say, will need to be taken. This truncated sequence of the auto-regressive estimates is used to form alternative estimates of the linear predictor, the autoregressive spectral density function and the innovation variance. Some of the asymptotic properties of these estimates are…

平稳纯非确定性过程移动平均表示的估计AI 解读

Estimation of the parameters of the moving average representation of a purely nondeterministic stationary time series is considered. A numerical example illustrating the estimation procedure is given.

平稳纯非确定性过程的移动平均表示估计AI 解读

Estimation of the parameters of the moving average representation of a purely nondeterministic stationary time series is considered. A numerical example illustrating the estimation procedure is given.

Wiener–Kolmogorov 预测器的渐近性质（一）AI 解读

Summary “Windowed” estimates of the spectral density function are used to carry out the factorization of the sample spectral density function and hence estimate the Wiener-Kolmogorov prediction coefficients. Expressions are derived for the asymptotic mean and variance of all the statistics necessary to estimate the predictor. The covariances and asymptotic normality of these estimates are also investigated. In particular, an expression for the asymptotic mean-square error or…

回归预测法与谱预测法的蒙特卡洛比较AI 解读

We consider the question of estimating the linear, least-squares predictor of the future values of a real-valued, discrete, purely nondeterministic, stationary time series from its known past. A method of estimating the prediction coefficients from the usual ‘windowed’ estimates of spectral density function is put forward and a Monte Carlo comparison of this method with the more usual regression method of finding the best fitting autoregressive process by recursively solving…

回归预测法与谱预测法的蒙特卡洛比较AI 解读

We consider the question of estimating the linear, least-squares predictor of the future values of a real-valued, discrete, purely nondeterministic, stationary time series from its known past. A method of estimating the prediction coefficients from the usual ‘windowed’ estimates of spectral density function is put forward and a Monte Carlo comparison of this method with the more usual regression method of finding the best fitting autoregressive process by recursively solving…