本文的目的在于，对于线性平稳时间序列的样本、自协方差、自相关和偏相关函数的渐近性质，给出一个比较系统的描述。
The aim of this paper is to give a systematic account of asymptotic properties of the sample autocovariance, autocorrelation and partial autocorrelation functions of linear stationary time series.
表明： 基于氨基酸组成和有偏自协方差函数为特征矢量的BP神经网络预测蛋白质二级结构含量的方法可有效提高预测精度。
It is shown that the BP neural network method combined with the amino-acid composition and the biased auto-covariance function features could effectively improve the prediction accuracy.
主要研究结果如下：1。基于几种非参数估计理论，构造了非参数自回归模型条件方差函数的非参数估计表达式。
The main contributions are as follows:1. Based on several nonparametric estimation theories, several estimation expressions of conditional heteroscedastic function are constructed.
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