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贝叶斯框架下归类、总体区分与概率确定中的影响观测检测AI 解读

Normal theory separation and allocation problems are discussed from a predictive point of view. Influence statistics are defined and employed to ascertain the impact that particular observations will have on the inferential goals—allocation of future observations, separation between populations, and the determination of probabilities for future cases. Methods are illustrated on a collection of financial data taken from Johnson and Wichern (1982).

Logistic 回归影响度量的另一种视角AI 解读

Several measures of influence for logistic regression have been suggested. These measures have been developed for the purpose of identifying observations which are influential relative to the estimation of the regression coefficients vector and the deviance. We propose measures for detecting influence relative to the determination of probabilities and the classification of future observations. The relationships among measures are indicated.

从预测视角识别与刻画回归中的影响观测AI 解读

Assuming a set of observations is available from the general linear model, and assuming prior information about parameters, we propose a method of assessing the influence of specified subsets of the data when the goal is to predict future observations.