In this paper a "compact least square method" is presented to overcome the ill-conditioned coefficient matrices in the linear regression models.
本文提出一种克服线性回归模型系数矩阵病态的“紧致最小二乘估计”。
The recognition problem is taken as one of classifying among multiple linear regression models, and sparse signal representation is used to solve this problem.
将识别问题看作是多个线性回归模型中的分类问题，并用稀疏表示理论解决这些问题。
This paper studies linear regression models with dependent errors, and we introduce the jackknifed least squares estimator and generalized jackknife least squares estimator.
在误差为相依的情况下，讨论了线性回归模型的刀切最小二乘估计与广义刀切最小二乘估计。
Two classes of fuzzy linear regression models are considered, one involves real design points and fuzzy number parameters, the other consists of fuzzy number observations and real parameters.
考虑两类模糊回归模型：一类是设计点为实数、参数为模糊数；另一类是观察值为模糊数、参数为实数。
Linear regression models are established for both logarithm of heavy metal content and normalized ratio emissivity and are used to verify the significance of the regression equations.
建立了土壤重金属含量的对数值与发射率标准化比值的线性回归模型，并对回归方程的显著性进行了检验。
Linear regression models are established for both logarithm of heavy metal content and normalized ratio emissivity and are used to verify the significance of the regression equations.
建立了土壤重金属含量的对数值与发射率标准化比值的线性回归模型，并对回归方程的显著性进行了检验。
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