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回归变量数量选择程序的近似效率
A simple approximation formula for the efficiency of the generalized final prediction error, (FPEα), criterion is obtained. In connexion with the approximate efficiency, a procedure for choosing α is proposed together with a further generalization of the FPEα criterion.
回归变量个数选择程序的近似效率
A simple approximation formula for the efficiency of the generalized final prediction error, (FPEα), criterion is obtained. In connexion with the approximate efficiency, a procedure for choosing α is proposed together with a further generalization of the FPEα criterion.
最优自回归谱估计
An asymptotic lower bound is obtained for the integrated relative squared error of autoregressive spectral estimate when the order of autoregression is selected. The bound is attained in the limit by the same selection as has been proposed for prediction.
回归变量的渐近最优选择
An asymptotically optimal selection of regression variables is proposed. The key assumption is that the number of control variables is infinite or increases with the sample size. It is also shown that Mallows's Cp', Akaike's FPE and aic methods are all asymptotically equivalent to this method.
线性过程参数估计中模型阶数的渐近有效选择
Let $\{x_t\}$ be a linear stationary process of the form $x_t + \Sigma_{1\leqslant i<\infty}a_ix_{t-i} = e_t$, where $\{e_t\}$ is a sequence of i.i.d. normal random variables with mean 0 and variance $\sigma^2$. Given observations $x_1, \cdots, x_n$, least squares estimates $\hat{a}(k)$ of $a' = (a_1, a_2, \cdots)$, and $\hat{\sigma}^2_k$ of $\sigma^2$ are obtained if the $k$th order autoregressive model is assumed. By using $\hat{a}(k)$, we can also estimate coefficients of…
用 Akaike 信息准则选择自回归模型阶数
The asymptotic distribution is obtained of the order of regression selected by Akaike's information criterion in autoregressive models. The asymptotic quadratic risks of estimates of regression parameters are evaluated when the order is selected by this method. Some results of computational experiments are given.
利用赤池信息准则选择自回归模型阶数
The asymptotic distribution is obtained of the order of regression selected by Akaike's information criterion in autoregressive models. The asymptotic quadratic risks of estimates of regression parameters are evaluated when the order is selected by this method. Some results of computational experiments are given.