Comparing Sub-Survival Functions in a Competing Risks Model

In the competing risks literature, one usually compares whether two risks are equal or whether one is more serious. In this paper, we propose tests for the equality of two competing risks against an ordered alternative specified by their sub-survival functions. These tests are naturally developed as extensions of those based on hazard rates and cumulative incidence functions. We note that the interpretation of the new test results is more direct compared to the situation when the hypotheses are framed in terms of their cumulative incidence functions. The proposed tests are of the Kolmogrov–Smirnov type, based on maximum differences between sub-survival functions. Our simulation studies indicate that they are excellent competitors of the existing tests, that are based mainly on differences between cumulative incidence functions. A numerical example will demonstrate the advantages of the proposed tests.     

Ordering pairwise comparison structures

Following an introduction to the merits of pairwise comparison methods, we present various ordering algorithms for complete binary preferential structures. These procedures generalize the well-known numbering algorithm to the intransitive case. A new form of independence of irrelevant alternatives is presented. Moreover, various other criteria and characterizations for these algorithms are presented. Aside from solving ranking problems and making explicit value criteria and structures of human preference, our algorithms are applicable to subjects such as task-sequencing and artificial intelligence projects.     

On the asymptotic distribution of a class of aligned rank order test statistics in randomized block designs

In the present investigation, the unconditional asymptotic distribution of a class of aligned rank order test statistics for randomized block designs is derived under the null hypothesis and for nearby alternatives, as the number of blocks tends to infinity. The proofs of these results are based on the asymptotic equivalence in quadratic mean between aligned observations and their ranks and thus are quite similar to the Hájek and SKidák (1967) approach.     

Unmasking the Trend Sought by Jonckheere-Type Tests for Right-Censored Data

Medical and epidemiological studies often involve groups of subjects associated with increasing levels of exposure to a risk factor. Survival of the groups is expected to follow the same order as the level of exposure. Formal tests for this trend fall into the regression framework if one knows what function of exposure to use as a covariate. When unknown, a linear function of exposure level is often used. Jonckheere-type tests for trend have generated continued interest largely because they do not require specification of a covariate. This paper shows that the Jonckheere-type test statistics are special cases of a generalized linear rank statistic with time-dependent covariates which unfortunately depend on the initial group sizes and censoring distributions. Using asymptotic relative efficiency calculations, the Jonckheere tests are compared to standard linear rank tests based on a linear covariate over a spectrum of shapes for the true trend.     

学习型组织:现代学校发展战略的理想选择

学习型组织理论是以系统动力学为理论基础创建的一种先进的管理理念。该理论以其思想的先进性、手段的时代性以及方法的务实性,为人们提供了一种全新的科学管理思想体系。构建学习型组织是适应现代学习化社会的需要,使学校教育教学活动的特征更体现了学习性机构的意义和价值。随着终身教育和学习化社会的普及,先进、科学、富于变革力量的学习型组织已经越来越成为现代学校管理模式和发展战略的理想选择。     

Modifications of the Mack-Wolfe umbrella tests for a generalized Behrens-Fisher problem

We are concerned with testing procedures for umbrella alternatives in the k-sample location problem without making the assumption that the underlying populations have the same shape. Modifications of the Mack-Wolfe tests are proposed for the cases when the peak of the umbrella is known or unknown. The proposed procedures are exactly distribution-free when the continuous populations have the same shape. The modified test for peak-known umbrella alternatives remains asymptotically distribution-free when the continuous populations are symmetric, but not necessarily with the same shape.     

On the estimation of reliabilty function in a Weibull lifetime distribution

The estimation of the reliability function of the Weibull lifetime model is considered in the presence of uncertain prior information (not in the form of prior distribution) on the parameter of interest. This information is assumed to be available in some sort of a realistic conjecture. In this article, we focus on how to combine sample and non-sample information together in order to achieve improved estimation performance. Three classes of point estimatiors, namely, the unrestricted estimator, the shrinkage estimator and shrinkage preliminary test estimator (SPTE) are proposed. Their asymptotic biases and mean-squared errors are derived and compared. The relative dominance picture of the estimators is presented. Interestingly, the proposed SPTE dominates the unrestricted estimator in a range that is wider than that of the usual preliminary test estimator. A small-scale simulation experiment is used to examine the small sample properties of the proposed estimators. Our simulation investigations have provided strong evidence that corroborates with asymptotic theory. The suggested estimation methods are applied to a published data set to illustrate the performance of the estimators in a real-life situation.     

Stochastic equivalence of ranking methods

The asymptotic behavior of linear rank statistics for comparing the locations of two populations, where the observations are ranked jointly with other populations, is considered. Under certain conditions, the asymptotic behavior of these statistics does not depend on which other populations are included in the ranking. In particular, the difference of a pair of these statistics, with the same score function, but based on two different rankings, converges to zero in probability under Pitman alternatives and Chernoff-Savage conditions on the scores and underlying distributions.     

A test for exponentiatity against hnbue alternatives

Neyman's asymptotically most powerful test in the class of al1 similar tests for testing exponentiality against a parametric class of Makeham alternatives is shown to be consistent for the much wider class of harmonic new better than used in expectation distributions.