The superiority of the classical least square method is lost due to the morbid of designed matrix in large-type linear regression analysis.
在大型线性回归分析中，由于设计矩阵的病态使得经典的最小二乘法失去了优良性。
To resolve the problem in the application of the models, the author expanded the classical factor set of models by introducing the concept of integral regression.
为解决模型实际应用中的问题，笔者引用了积分回归的概念，对传统模型的因子集进行了扩充。
The radium of blasting cavity, stress peak value and action time of stress waves are validated by the theory of classical explosion mechanics and the way of curve regression.
借助经典的爆炸力学理论和回归拟合曲线方法，验证了爆炸空腔大小、应力波的应力峰值、应力波的作用时间。
Objective To relax linear assumption of explanatory variable in classical linear model and explore semiparametric regression model.
目的放宽经典线性模型中的解释变量的线性假定和探讨半参数回归分析模型。
Objective To improve on classical least squares estimate of regression analysis and explore nonparametric smoothing spline regression analysis.
目的改进回归分析的经典最小二乘估计方法和探讨光滑样条非参数回归分析方法。
Based on the classical model of liner regression and considering effects of fuzzy factors on load forecasting, a liner regression method of tow-layer fuzzy factors for load forecasting is established.
基于经典的线性预测回归模型，引入了模糊因素对预测结果的影响，构成了二级模糊因素的多元线性回归法。
Regarding the classical models of regression analysis, people generally assume that its response variable is the continual variable.
对于经典回归分析模型来说，人们一般都是假定其响应变量为连续型变量。
In addition, an adaptive kernel relevance vector machine based on PSO is presented to deal with the problem that the regression performance of classical RVM is often influenced by kernel parameters.
此外，针对相关向量机回归计算结果受核参数影响较大的问题，本文还提出一种基于微粒群算法的相关向量机核参数自适应优化方法。
The paper proposes application of Wavelet Neural Network in high-frequency time series calendar effects' study. At last, the paper proves that WNN is better than classical FFF regression.
提出了用小波神经网络（WNN）来定量研究高频金融时间序列“日历效应”，通过比较发现WNN是比弹性傅立叶形式（FFF）回归技术更具优势的方法。
The paper proposes application of Wavelet Neural Network in high-frequency time series calendar effects' study. At last, the paper proves that WNN is better than classical FFF regression.
提出了用小波神经网络（WNN）来定量研究高频金融时间序列“日历效应”，通过比较发现WNN是比弹性傅立叶形式（FFF）回归技术更具优势的方法。
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