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利用连续有限差分估计标准误AI 解读

Summary Finite differences have been used to estimate random error in time series, the process being referred to as the variate-difference method (Kendall, 1948; Tintner, 1940). In this note an examination is made of the relative efficiencies of the various estimates of standard error obtained from mean differences and mean square differences (assuming a normal law), and to apply the results to the estimation of the standard error in the fitting of polynomials by means of…

系统抽样文献综述AI 解读

Summary The literature relating to systematic sampling shows the interplay between practical needs and the provision of statistical techniques for their satisfaction. Problems of systematic sampling occur more frequently than is generally realized and, since many of the techniques are still far from satisfactory, the situation offers great incentive to further development.

区间的随机分割：第二部分AI 解读

Summary In a previous paper the theory of a test for the random distribution of events in time was considered. In the present paper a model is set up for a process in which events occur in time in a dependant manner. Using this model as an alternative hypothesis the asymptotic efficiency of the previously considered test is compared with that of a test proposed by M. S. Bartlett and it is found that the latter is considerably more efficient as well as being easier to use.

若干简单随机过程AI 解读

Summary Recently Janossy in his treatment of the stochastic problem of cosmic radiation employed “the method of regeneration points”—a method used earlier by Bellman and Harris and by C. Palm. In this paper, we consider some simple stochastic processes using this method. The simplified statistical problem of cosmic radiation is discussed in detail.

多元回归的计算与解释AI 解读

Summary Full practical directions are given for the expeditious calculation of regression equations, which are built up by successive insertion of additional variates. The computer can readily measure the contribution made by each new variate to the goodness of fit, omit variates which do not make a significant contribution, and select that group of independent variates which together give the lowest residual mean square in the dependent variate. The statistical properties…

更实用的矩阵求逆方法AI 解读

Summary Two methods are illustrated for the inversion of matrices, with special attention to the arrangement of the work on the computing sheet, and the application of frequent and powerful checks. The necessity for checking cannot be overemphasized, and if the checks are too widely spaced an early mistake may cause a lot of unnecessary work. This paper, like the earlier one (Fox, 1950) has been written for the computer, and the methods given in the two papers provide him…

随机间隔需要照管的机器的生产率AI 解读

Summary In many industrial processes an operative attends a number of machines which stop from time to time. Each time a machine stops the operative has to do a certain amount of work before it can be restarted. If at any time two or more machines are stopped simultaneously, there will be a loss of production due to the period the machines have to wait for attention. In this paper we derive methods of estimating this loss of production when the operative has no duties away…

利用 Cholesky 方法进行正交归一多项式最小二乘拟合AI 解读

Summary If a set of normal equations, derived from the least squares fitting of a linear representation to a set of observations, is solved by the Choleski method of solving a system of linear simultaneous algebraic equations involving a symmetric matrix, a certain vector is obtained as an intermediate step in the solution. The squares of the elements of this vector are the components in the resolution of the total sum of squares of the observations into squares…

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自回归模型中 Quenouille 检验的扩展AI 解读

M. S. Bartlett, P. H. Diananda; Extensions of Quenouille’s Test for Autoregressive Schemes, Journal of the Royal Statistical Society Series B: Statistical

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第二部分：从心理学家视角评价因子分析AI 解读

Summary of Contents The need for such a method—Some historical points—What goes in and what does not—Some aspects of method—Q and R techniques—Burt’s reconciliation of Q and R and their relationship to analysis of variance—Stephenson’s treatment of trait universes—The importance of factors—Conclusions.

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