主变量筛选方法

本文利用矩阵的扫描运算，提出一种对高维随机向量X=（x1,x2,…,xp)‘进行降维处理的实用方法-主变量筛选方法，给出了该方法的理论依据、直观解释、算法及数值例子。该方法是不同于主成分分析法的一种降维方法。特别，当变量X多重相关性突出时，本文方法效果显著。  

基于奇异值分解的岭型回归(英文)

本文基于设计阵的奇异值分解(SVD),从LS估计出发,应用岭回归估计方法,构造了回归系数的一个新的有偏估计,称为基于SVD的岭型回归估计,简称RRSVD估计,讨论了其性质和偏参数的选取问题,得到了许多重要结论.计算结果表明,在设计阵呈病态时,RRS善岭回归估计.  

线性模型参数的稳健化有偏估计

本文讨论复共线性和粗差同时存在时线性模型的参数估计问题，基于等价权原理提出了一个稳健有偏估计类（稳健压缩估计），并且建立了稳健压缩估计的计算方法，为了满足实际问题的需要，构造了许多很有意义的稳健有偏估计，例如稳健岭估计、稳健主成分估计，稳健组合主成估计、稳健单参数主成分估计、稳健根方估计等等，最后通过一个算例表明，本文提出的稳健有偏估计具有既可克服复共线性影响又可抵抗粗差干扰的良好性质。  

A stochastic restricted ridge regression estimator

Groß [J. Groß, Restricted ridge estimation, Statistics & Probability Letters 65 (2003) 57–64] proposed a restricted ridge regression estimator when exact restrictions are assumed to hold. When there are stochastic linear restrictions on the parameter vector, we introduce a new estimator by combining ideas underlying the mixed and the ridge regression estimators under the assumption that the errors are not independent and identically distributed. Apart from [J. Groß, Restricted ridge estimation, Statistics & Probability Letters 65 (2003) 57–64], we call this new estimator as the stochastic restricted ridge regression (SRRR) estimator. The performance of the SRRR estimator over the mixed estimator in respect of the variance and the mean square error matrices is examined. We also illustrate our findings with a numerical example. The shrinkage generalized least squares (GLS) and the stochastic restricted shrinkage GLS estimators are proposed.  

带线性约束的回归模型参数的Liu估计方法

针对一般带约束的最小二乘估计(ORLSE)在参数估计中处理复共线性的不足,通过引入附加随机线性约束,提出了约束Liu型估计方法.经理论证明,此方法在均方误差下较已经提出的ORLSE和约束岭估计(RRE)效果更好,并且它可以作为两种方法的推广形式.讨论了其中k, d的确定原则,并将此方法推广到了广义形式和典则形式下的估计公式.  

Improved estimation under collinearity and squared error loss

This paper examines the performance of several biased, Stein-like and empirical Bayes estimators for the general linear statistical model under conditions of collinearity. A new criterion for deleting principal components, based on an unbiased estimator of risk, is introduced. Using a squared error measure and Monte Carlo sampling experiments, the resulting estimator's performance is evaluated and compared with other traditional and non-traditional estimators.  

追加数据对条件指数的影响<英>

克服线性回归模型中设计矩阵的复共线性,主要有三种方法:追加数据,自变量选择和非最小二乘法,本文研究追加数据在减少条件数中的作用,我们的研究表明,在可能情况下适当地选择追加数据,设计矩阵的条件指数可以被减少.我们用在回归最优设计中广泛被研究过的Gaylor-Merrill数据说明了本文理论结果的实用意义.  

岭回归在石油钻井工程中的应用

在石油钻井工程中，井壁坍塌的状况即井径的扩大与缩小，与地层中泥页岩的各种理化性质有关，而这些理化参数又存在于一定程度的相关性。本文利用岭回归分析数据进行了分析与处理，建立了井径扩大的数学数学模型，确定了影响井径的主要理化指标，为解决井壁坍塌提供了理论依据。  

r-k Class estimation in regression model with concomitant variables

We treat with the r-k class estimation in a regression model, which includes the ordinary least squares estimator, the ordinary ridge regression estimator and the principal component regression estimator as special cases of the r-k class estimator. Many papers compared total mean square error of these estimators. Sarkar (1989, Ann. Inst. Statist. Math., 41, 717–724) asserts that the results of this comparison are still valid in a misspecified linear model. We point out some confusions of Sarkar and show additional conditions under which his assertion holds.  

Comparisons among some estimators in misspecified linear models with multicollinearity

In this paper we deal with comparisons among several estimators available in situations of multicollinearity (e.g., the r-k class estimator proposed by Baye and Parker, the ordinary ridge regression (ORR) estimator, the principal components regression (PCR) estimator and also the ordinary least squares (OLS) estimator) for a misspecified linear model where misspecification is due to omission of some relevant explanatory variables. These comparisons are made in terms of the mean square error (mse) of the estimators of regression coefficients as well as of the predictor of the conditional mean of the dependent variable. It is found that under the same conditions as in the true model, the superiority of the r-k class estimator over the ORR, PCR and OLS estimators and those of the ORR and PCR estimators over the OLS estimator remain unchanged in the misspecified model. Only in the case of comparison between the ORR and PCR estimators, no definite conclusion regarding the mse dominance of one over the other in the misspecified model can be drawn.