1) Ridged shrunken principal cormponents estimate
岭型压缩主成分估计
2) combining ridge and principal components estimate
岭型主成分估计
1.
This paper discusses its superiority of the optimal and classical predictors based on the combining ridge and principal components estimate.
针对有偏降维估计的预测问题,以岭型主成分估计为基础,对广义线性回归模型{y=Xβ+ε,ε-N(0,σ2∑)}的最优预测量与经典预测量的最优性判别问题进行讨论。
2.
The variance optimality of combining ridge and principal components estimate is discussed in the class of reduced-dimension estimates.
研究岭型主成分估计在降维估计类中的方差最优性,证明了它的方差阵在降维估计类中最小,方差阵的特征值最小,方差和及方差积最小。
3.
おhis paper discusses the variance property of combining ridge and principal components estimate in the class of reduceddimension estimators.
讨论了岭型主成分估计在一类降维估计中的方差性质，证明了在一定条件下岭型主成分估计的方差和最小。
3) shrunken principal estimator
压缩主成分估计
1.
Considering the generalized linear regression model and its prediction problem of biased estimation,this paper discusses its superiority of the optimal and classical predictors based on the shrunken principal estimator by criteria of mean dispersion error matrix and generalized risk function.
本文以压缩主成分估计为基础,对广义线性模型的最优预测与经典预测的最优性判别问题进行了讨论,获得了在离差矩阵判别准则和广义风险函数判别准则下判断两类预测量最优性的一个充分条件,为进一步研究基于有偏估计关于两类预测量的最优性判别问题提供了一种方法和思路。
2.
The paper discusses some characteristics of the shrunken principal estimator,proves that the shrunhen principal estimate is better than LS estimator under GMSE and Pitman抯 Measure of Clossness and makes some additions to Y抯 prediction characteristic
讨论了压缩主成分估计的一些性质,证明了在一定条件下,此估计比最小二乘估计有更小的广义均方误差并且在PC准则下也优于最小二乘估计。
3.
The paper discusses some characteristics of the shrunken principal estimator and proves that the shrunhen principal estimate is better than LS estimator under GMSM and Pitman’s Measure of Clossness.
讨论了压缩主成分估计的若干性质,证明了在一定条件下此估计比最小二乘估计有更小的广义均方误差,并且在PC准则下也优于最小二乘估计。
4) combining generalized ridge and principal components estimator
广义岭型主成分估计
1.
The combining generalized ridge and principal components estimator of regression coefficient in growth curve model;
增长曲线模型中回归系数的广义岭型主成分估计
5) ridge combined principal component estimate
岭型组合主成分估计
1.
A new estimate, ridge combined principal component estimate (R CP), is proposed for the regression coefficients of a linear model Some properties including admissibility,excellence property of the estimate are studied Several selecting methods for using the ridge parameters are also give
提出了线性模型回归系数的一种新的估计——岭型组合主成分估计 ,讨论了岭型组合主成分估计的可容许性、优良性等性质 ,并给出了应用时岭参数的几种选取方
6) classification compression principal component estimate
分组压缩主成份估计
1.
This paper considers the classification compression principal component estimate of regression coefficient in growth curve model and proves that it is superior to least squares estimate.
对增长曲线模型的回归系数提出了分组压缩主成份估计，并证明了分组压缩主成份估计优于最小二乘估计。
补充资料：岭估计
分子式：
CAS号：
性质：统计学中有偏估计的一种方法。主要针对回归分析中存在的共线性而造成的经典最小二乘估计的参数不稳定而提出的一种改进方法。岭回归通过对回归系数矩阵的对角元素进行微扰，即将最小二乘估计式=(XtX)-1XtY改为=(XtX+kI)-lxtY，从而减少估计参数的均方误差。因为这种微扰失去了经典最小二乘估计的无偏性，故有有偏估计之称。
CAS号：
性质：统计学中有偏估计的一种方法。主要针对回归分析中存在的共线性而造成的经典最小二乘估计的参数不稳定而提出的一种改进方法。岭回归通过对回归系数矩阵的对角元素进行微扰，即将最小二乘估计式=(XtX)-1XtY改为=(XtX+kI)-lxtY，从而减少估计参数的均方误差。因为这种微扰失去了经典最小二乘估计的无偏性，故有有偏估计之称。
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参考词条