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对称性下的扩展统计建模及其与量子力学的联系AI 解读

We derive essential elements of quantum mechanics from a parametric structure extending that of traditional mathematical statistics. The basic setting is a set $\mathcal{A}$ of incompatible experiments, and a transformation group G on the cartesian product Π of the parameter spaces of these experiments. The set of possible parameters is constrained to lie in a subspace of Π, an orbit or a set of orbits of G. Each possible model is then connected to a parametric Hilbert…

仅少数成分相关时的预测方法比较AI 解读

We consider prediction in a multiple regression model where we also look on the explanatory variables as random. If the number of explanatory variables is large, then the common least squares multiple regression solution may not be the best one. We give a methodology for comparing certain alternative prediction methods by asymptotic calculations and perform such a comparisons for four specific methods. The results indicate that none of these methods dominates the others, and…

基于相关成分的最大似然回归AI 解读

SUMMARY Most regression methods for multicollinear data are only based on assumptions about the conditional distribution of the dependent variable, given the explanatory variables. Here we propose a new prediction method taking a joint multinomial distribution for all the variables as the starting point. A hypothesis is formulated stating that only a fixed small number of components constructed from the explanatory variables is relevant for prediction, and…