论文检索

离散程度矩阵与主成分的稳健估计AI 解读

This paper uses Monte Carlo methods to compare the performances of several robust procedures for estimating a correlation matrix and its principal components. The estimators are formed either from separate bivariate analyses or by simultaneous manipulation of all variables by using techniques such as multivariate trimming and M-estimation. The M-estimators stand up exceptionally well. They and the multivariate trimming procedure are especially effective at estimating the…

相关系数的稳健估计与异常值检测AI 解读

Two graphical methods are proposed for identifying bivariate observations that may unduly influence the sample correlation coefficient. Secondly, robust estimators of correlation are developed and a Monte Carlo comparative study is made of these and other well-known estimators. Also considered are methods for developing positive-definite estimates of correlation matrices and extensions of robustness to other problems such as regression are mentioned.

多组变量的典型分析AI 解读

Five extensions of the classical two-set theory of canonical correlation analysis to three or more sets are considered. For each one, a model of the general principal component type is constructed to aid in motivating, comparing and understanding the methods. Procedures are developed for finding the canonical variables associated with the different approaches. Some practical considerations and an example are also included.