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一类位置与尺度的非参数检验AI 解读

A class of two-sample nonparametric tests for location and scale shift, simultaneously, is proposed. The asymptotic distribution of the proposed class is obtained both under the hypothesis and alternative. Using this result it is shown how the asymptotic relative efficiency of tests in the proposed class can be obtained.

若干多样本非参数尺度检验的小样本表现AI 解读

Several nonparametric and parametric statistics for testing the equality of scale parameters are compared with respect to their power and stability of error rates. When the populations have mass confined to the positive axis, the c-sample analog of the sum of squared ranks test and the Kruskal-Wallis test are found to be very robust and powerful. When the populations are symmetrical, the c-sample analogs of the two-sample Mood and two-sample Ansari-Bradley tests are robust…

若干多样本非参数尺度检验的小样本表现AI 解读

Several nonparametric and parametric statistics for testing the equality of scale parameters are compared with respect to their power and stability of error rates. When the populations have mass confined to the positive axis, the c-sample analog of the sum of squared ranks test and the Kruskal-Wallis test are found to be very robust and powerful. When the populations are symmetrical, the c-sample analogs of the two-sample Mood and two-sample Ansari-Bradley tests are robust…

摘要暂缺

未知累积量下的 Edgeworth 展开AI 解读

In this paper a new method of approximating one distribution by another is introduced. The method is essentially a modification of the Edgeworth technique which eliminates the necessity of knowing the cumulants of the distributions involved.

最优线性递归估计AI 解读

This article presents a matrix formulation of recursive forms for best linear unbiased estimators [Xcirc]N of the parameter vector x in the linear model yi=hix+ei, i = 1,2, ···, N when the observation vectors yi are correlated. If data are collected in sequence one can formulate recursive forms of the estimator [Xcirc]N so that it is not necessary to store all the previous data but only previous estimates and current data. This requires less storage space to obtain best…

最优线性递推估计AI 解读

This article presents a matrix formulation of recursive forms for best linear unbiased estimators [Xcirc]N of the parameter vector x in the linear model yi=hix+ei, i = 1,2, ···, N when the observation vectors yi are correlated. If data are collected in sequence one can formulate recursive forms of the estimator [Xcirc]N so that it is not necessary to store all the previous data but only previous estimates and current data. This requires less storage space to obtain best…

高斯—马尔可夫定理的一个推广AI 解读

This paper contains a generalization of the Gauss Markov Theorem based on the properties of the generalized inverse of a matrix as defined by Penrose. A minimum variance vector estimate of a parameter vector x is given for the linear model of less than full rank. Since linear unbiased estimates may not always exist for this case the unbiased constraint is replaced by the more general constraint that the norm is minimized.