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作者：GLASER, RE
摘要：Exact expressions are obtained for the density and distribution of Bartlett''s (1937) statistic for testing for homogeneity of variances of k normal populations, when random samples of not necessarily the same size are taken. The latter expression facilitates computation of exact critical values. An illustrative computation is presented.
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作者：HALL, WJ; WELLNER, JA
摘要：For arbitrarily right-censored data, the Kaplan-Meier product-limit estimator .**GRAPHIC**. provides a nonparametric estimate of the survival function S0 = 1 - F0. Large-sample simultaneous confidence bands are provided for S0, centered at .**GRAPHIC**. The derivation uses the weak convergence of .**GRAPHIC**. - S0(t)}, on a finite interval, to a Gaussian process, a theorem of Breslow and Crowley (1974), and transforms both the time and space axes of the limiting process to achieve a Brownian ...
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作者：LUSTBADER, ED
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作者：NEWBOLD, P
摘要：Two tests for the adequacy of a fitted ARMA [autoregressive-moving average model] (p,q) model, based, respectively, on comparison through the Lagrange multiplier test with an ARMA (p + k, q) model and on analysis of the 1st k residual autocorrelations from the estimated model, are equivalent.
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作者：GATES, DJ; WESTCOTT, M
摘要：Consider N points distributed independently and uniformly on the surface of a sphere. Improved upper and lower bounds are given for the distribution of the minimum interpoint angular distance.
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作者：LIN, CC; MUDHOLKAR, GS
作者单位：University of Rochester
摘要：The mean and the variance of a random sample are independently distributed if and only if the parent population is normal. This characterization is used as a basis for developing a test, termed the Z test, for the composite hypothesis of normality against asymmetric alternatives. The null distribution of the Z test statistic is for practical purposes satisfactorily approximated by a normal distribution for samples of size 5 up to 100. The large sample null distribution and the consistency of t...
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作者：SCHOENFELD, D
摘要：A class of omnibus chi-squared goodness-of-fit is presented for the model, relating failure time to covariate values, proposed by Cox (1972). These tests are based on the expected and observed frequency that a data point, representing a failure with associated covariates, falls into one of L mutually exclusive categories. Different partitions of the space of covariate values and failure time will yield different tests. Certain partitions are suggested for the goodness-of-fit problems commonly ...
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作者：AITCHISON, J; SHEN, SM
摘要：The logistic transformation applied to a d-dimensional normal distribution produces a distribution over the d-dimensional simplex which can sensibly be termed a logistic-normal distribution. Such distributions, implicitly used in a number of recent applications, are here given a formal identity and some useful properties are recorded. A main aim is to extend the area of application from the restricted role as a substitute for the Dirichlet conjugate prior class in the analysis of multinomial a...
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作者：SIEGMUND, D
摘要：Sequential .chi.2 and F tests are proposed for comparing more than 2 treatments (e.g., clinical trials). Extending earlier results approximations to the significance level and power of the test and approximate confidence intervals for the noncentrality parameter .theta. were obtained. For each fixed .theta. a confidence region is given for the angle .omega. which the treatment effect vector in canonical coordinates makes with a fixed direction, and hence an overall confidence region is obtaine...
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作者：COX, MAA; PLACKETT, RL
摘要：The analysis of some contingency tables is based on hierarchical log linear models. Inferences about sets of parameters can be made from conditional distributions. Three ways of improving the asymptotic methods generally used in practice are discussed. They comprise better approximations to moments of cell frequencies than those derived from the iterative scaling procedure, exact distributions obtained by enumerating all tables consistent with given marginal totals and simulated random samplin...
