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稀疏充分降维AI 解读

Existing sufficient dimension reduction methods suffer from the fact that each dimension reduction component is a linear combination of all the original predictors, so that it is difficult to interpret the resulting estimates. We propose a unified estimation strategy, which combines a regression-type formulation of sufficient dimension reduction methods and shrinkage estimation, to produce sparse and accurate solutions. The method can be applied to most existing sufficient…

偏逆回归AI 解读

In regression with a vector of quantitative predictors, sufficient dimension reduction methods can effectively reduce the predictor dimension, while preserving full regression information and assuming no parametric model. However, all current reduction methods require the sample size n to be greater than the number of predictors p. It is well known that partial least squares can deal with problems with n < p. We first establish a link between partial least squares and…

两组排序一致性检验的若干性质AI 解读

The statistic L, introduced to test for concordance within and between two groups of rankings of k objects is shown to be related to several measures of internal rank correlation. It has been previously shown that Kendall's W is proportional to the average of the rank correlations of all pairs of rankings in a single group and that Page's L is essentially the average of the rank correlations of an external ranking with each ranking in a group suggested by Lyerly. Similarly L…