Comparison of two independent life tests subject to type II censoring

The problem of comparing two life distributions is considered. It Is assumed that each life test Is stopped after a predetermined number of failures without regard to the number of failures which have occurred in the other life test. A generalized Wilcoxon-rlann-Whitney test is proposed. Small and large sample distributional results are obtained for the test statistic. Under the assumption that the life distributions differ by at most a scale parameter an exact confidence interval for that parameter is obtained. An algorithm for computing the interval is given.     

A Monte Carlo study of traditional and rank-based picked-points analyses

In analysis of covariance with heteroscedastic slopes a picked-points analysis is often performed. Least-squares based picked-points analyses often lose efficiency (at times substantial) for nonnormal error distributions. Robust rank-based picked-points analyses are developed which are optimizable for heavy-tailed and/or skewed error distributions. The results of a Monte Carlo investigation of these analyses are presented. The situations include the normal model and models which violate it in one or several ways. Empirically the rank-based analyses appear to be valid over all these situations and more powerful than the least squares analysis for all the nonnormal models, while losing little efficiency at the normal model.     

我国化工企业的财务预警模型构建及其检验——基于Logistic回归方法

借助Wilcoxon秩检验比较11家ST与54家非ST化工企业之间在53个财务比率上存在的显著差异,筛选出能够较好反应企业财务状况的33个财务比率,利用因子分析方差最大化旋转对企业财务信息进行统计处理,分析得出主因子得分,构建相应的Logistic回归模型,得出具有较高准确性的化工企业财务困境预警系统,发现企业债务状况、获利状况、权益增值能力对企业财务困境有显著的预警作用。     

Exact tests based on the Baumgartner-Weiß-Schindler statistic—A survey

It is the purpose of this paper to review recently-proposed exact tests based on the Baumgartner-Weiß-Schindler statistic and its modification. Except for the generalized Behrens-Fisher problem, these tests are broadly applicable, and they can be used to compare two groups irrespective of whether or not ties occur. In addition, a nonparametric trend test and a trend test for binomial proportions are possible. These exact tests are preferable to commonly-applied tests, such as the Wilcoxon rank sum test, in terms of both type I error rate and power.     

On the Iteratively Reweighted Rank Regression Estimator

The finite sample performance of the rank estimator of regression coefficients obtained using the iteratively reweighted least squares (IRLS) of Sievers and Abebe (2004) is evaluated. Efficiency comparisons show that the IRLS method does quite well in comparison to least squares or the traditional rank estimates in cases of moderate-tailed error distributions; however, the IRLS method does not appear to be suitable for heavy-tailed data. Moreover, our results show that the IRLS estimator will have an unbounded influence function even if we use an initial estimator with a bounded influence function.     

Weighted Wilcoxon Estimates for Autoregression

This paper explores the class of weighted Wilcoxon (WW) estimates in the context of autoregressive parameter estimation, giving special attention to three sub-classes of so-called WW-estimates. When the weights are constant, the estimate is equivalent to using Jaeckel's estimate with Wilcoxon scores. The paper presents asymptotic linearity properties for the three sub-classes of WW-estimates. These properties imply that the estimates are asymptotically normal at rate n ^½. Tests of hypotheses as well as standard errors for confidence interval procedures can be based on such results. Furthermore, the estimates can be computed with an L ₁ regression routine once the weights have been calculated. Examples and a Monte Carlo study over innovation and additive outlier models suggest that WW-estimates can be both robust and highly efficient.     

One-sample location tests for multilevel data

In this paper, we consider testing the location parameter with multilevel (or hierarchical) data. A general family of weighted test statistics is introduced. This family includes extensions to the case of multilevel data of familiar procedures like the t, the sign and the Wilcoxon signed-rank tests. Under mild assumptions, the test statistics have a null limiting normal distribution which facilitates their use. An investigation of the relative merits of selected members of the family of tests is achieved theoretically by deriving their asymptotic relative efficiency (ARE) and empirically via a simulation study. It is shown that the performance of a test depends on the clusters configurations and on the intracluster correlations. Explicit formulas for optimal weights and a discussion of the impact of omitting a level are provided for 2 and 3-level data. It is shown that using appropriate weights can greatly improve the performance of the tests. Finally, the use of the new tests is illustrated with a real data example.     

On the power of the t-test and some rank tests when outliers may be present

The power of some rank tests, used for testing the hypothesis of shift, is found when the underlying distributions contain outliers. The outliers are assumed to occur as the result of mixing two normal distributions with common variance. A small sample case shows how the scores for the rank tests are found and the exact power is computed for each of these rank tests. A Monte Carlo study provides an estimate of the power of the usual two sample t-test.     

The limit test statistic distribution of the maximum value test for right-censored data

ABSTRACT

In this paper, the maximum value test is proposed and considered for two-sample problem solving with lifetime data. This test is a distribution-free test under non-censoring and is a not distribution-free test under censoring. The formula of the limit distribution of the proposed maximal value test is represented in the general case. The distribution of the test statistic has been studied experimentally. Also, we propose the estimate of a p-value calculation of the maximum value test instead of the Monte-Carlo simulation. This test is useful and applicable in case of choosing among the logrank test, the Cox–Mantel test, the Q test and Generalized Wilcoxon tests, for instance, the Gehan's Generalized Wilcoxon test and the Peto and Peto's Generalized Wilcoxon test.     

A new two-sample test for choosing between log-rank and Wilcoxon tests with right-censored data

When differences of survival functions are located in early time, a Wilcoxon test is the best test, but when differences of survival functions are located in late time, using a log-rank test is better. Therefore, a researcher needs a stable test in these situations. In this paper, a new two-sample test is proposed and considered. This test is distribution-free. This test is useful for choosing between log-rank and Wilcoxon tests. Its power is roughly the maximal power of the log-rank test and Wilcoxon test.