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作者：STUTE, W; WANG, JL
作者单位：University of California System; University of California Davis
摘要：Let X1, X2, ... be a sequence of i.i.d. random variables with d.f. F. We Observe Z(i) = min(X(i), Y(i)) and delta(i) = 1(Xi less-than-or-equal-to Yi), where Y1, Y2, ... is a sequence of i.i.d. censoring random variables. Denote by F(n) the Kaplan-Meier estimator of F. We show that for any F-integrable function phi, integral phi dF(n) converges almost surely and in the mean. The result may be applied to yield consistency of many estimators under random censorship.
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作者：DAWID, AP; WANG, JL
作者单位：East China Normal University
摘要：We investigate the problem of fiducial prediction for unobserved quantities within the framework of the functional model described previously by Dawid and Stone. It is supposed that these are related to a completely unknown parameter by means of a regular functional model, and that the observations are either given as known functions of the predictands, or are themselves related to them by means of a functional model. We develop algebraic conditions which allow the application of fiducial logi...
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作者：MAGULURI, G
摘要：Clayton's model for association in bivariate survival data is both of intrinsic importance and an interesting example of a semiparametric estimation problem, that is, a problem where inference about a parameter is required in the presence of nuisance functions. The joint distribution of the two survival times in this model is absolutely continuous and a single parameter governs the association between the two survival times. In this paper we describe an algorithm to derive the asymptotic lower...
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作者：BOOTH, JG; HALL, P
作者单位：Australian National University; Commonwealth Scientific & Industrial Research Organisation (CSIRO)
摘要：In a recent paper, C. F. J. Wu showed that the jackknife estimator of a distribution function has optimal convergence rate O(n-1/2), where n denotes the sample size. This rate is achieved by retaining O(n) data values from the original sample during the jackknife algorithm. Wu's result is particularly important since it permits a direct comparison of jackknife and bootstrap methods for distribution estimation. In the present paper we show that a very simple, nonempirical modification of the ja...
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作者：KURIKI, S
摘要：Let H and G be independently distributed according to the Wishart distributions W(m)(M, PHI) and W(m)(N, PSI), respectively. We derive the limiting null distributions of the likelihood ratio criteria for testing H-0: PHI = PSI against H-1 - H-0 with H-1: PHI greater-than-or-equal-to PSI; and for testing H-0(R): PHI greater-than-or-equal-to PSI rank(PHI - PSI) less-than-or-equal-to R (for given R) against H-1 - H-0(R). They are particular cases of the chi-bar-squared distributions.
