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多元分析中的缺失值AI 解读

Summary This paper presents computational results for some alternative methods of analysing multivariate data with missing values. We recommend an algorithm due to Orchard and Woodbury (1972), which gives an estimator that is maximum likelihood when the data come from a multivariate normal population. We include a derivation of the estimator that does not assume a multivariate normal population, as an iterated form of Buck's (1960) method. We derive an approximate method of…

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多元分析中的变量剔除AI 解读

In many multivariate situations we are presented with more variables than we would like, and the question arises whether they are all necessary and if not which can be discarded. In this paper we consider two such situations. (a)Regression analysis. The problem here is whether any variables can be discarded as adding little or nothing to the accuracy with which the regression equation correlates with the dependent variable. (b)Interdependence analysis. The problem is whether…

非线性估计中的置信区域AI 解读

Summary The statistical properties of the approximate confidence regions for nonlinear estimation based on the likelihood ratio criterion are considered, assuming normal variation of errors. It is suggested that these approximate confidence regions are preferable to any known exact confidence regions for this problem, provided that the model is only moderately non-linear. A numerical measure of the non-linearity of a model (as a function of the experimental design and the…