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层均值函数的 Jackknife 估计AI 解读

SUMMARY The jackknife method of investigating and reducing the bias in nonlinear estimates of parameters is applied to stratified samples from a multivariate population of finite size. Attention is focused on estimators that can be expressed as functions of sample means. As in other applications of the jackknife method, an approximate relationship between the bias in an estimator and the sample size is exploited to reduce this bias by employing a linear combination of an…

分层子样本数据的方差分析AI 解读

For data selected from a finite population by stratified random sampling with equal probabilities and then randomly and independently assigned within each stratum to subsamples of a fixed number, methods are proposed for the analysis of variance and for testing differences between stratum means, differences between subsample means, and the difference between the sample mean and the mean of the population. The analysis is based on a model for which it is specified that…

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关于平方根坐标图的札记AI 解读

F. R. MACAULAY' and other writers2 have noted a tendency for changes in the square roots of common-stock prices to be constant regardless of price level. This phenomenon has naturally suggested that when such prices are represented graphically the charts be designed so that vertical distances from the origin are proportional to the square roots of the prices indicated in the margins. There is some reason to believe that charts of this kind might also be useful in plotting…

忽略变量的线性回归函数AI 解读

This article discusses some properties of the computed Y values obtained by fitting a linear regression function to independent observations by the method of least squares. For the general case where the form of the fitted function may not be correct it is proved that (a) the sampling variance of the computed values and of the residual differences is the same as for the special case where the form of the fitted function is correct, and (b) the mean square bias of the set of…

含遗漏变量的线性回归函数AI 解读

This article discusses some properties of the computed Y values obtained by fitting a linear regression function to independent observations by the method of least squares. For the general case where the form of the fitted function may not be correct it is proved that (a) the sampling variance of the computed values and of the residual differences is the same as for the special case where the form of the fitted function is correct, and (b) the mean square bias of the set of…

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