On U-statistics and von Mises statistics for a special class of Markov chains

We derive the Berry-Esséen theorem with optimal convergence rate for U-statistics and von Mises statistics associated with a special class of Markov chains occuring in the theory of dependence with complete connections.     

A Perturbation Approach to Outlier Detection in Two-Way Contingency Tables

In order to identify outliers in contingency tables, we evaluate the derivatives of the perturbation-formed surface of the Pearson goodness-of-fit statistic. The resulting diagnostics are shown to be less susceptible to masking and swamping problems than residual-based measures. A Monte Carlo study further confirms the effectiveness of the proposed diagnostics.     

Two-Tailed p-Values and Coherent Measures of Evidence

ABSTRACT

In a test of significance, it is common practice to report the p-value as one way of summarizing the incompatibility between a set of data and a proposed model for the data constructed under a set of assumptions together with a null hypothesis. However, the p-value does have some flaws, one being in general its definition for two-sided tests and a related serious logical one of incoherence, in its interpretation as a statistical measure of evidence for its respective null hypothesis. We shall address these two issues in this article.     

A nonparametric test to compare survival distributions with covariate adjustment

Summary.  The analysis of covariance is a technique that is used to improve the power of a k -sample test by adjusting for concomitant variables. If the end point is the time of survival, and some observations are right censored, the score statistic from the Cox proportional hazards model is the method that is most commonly used to test the equality of conditional hazard functions. In many situations, however, the proportional hazards model assumptions are not satisfied. Specifically, the relative risk function is not time invariant or represented as a log-linear function of the covariates. We propose an asymptotically valid k -sample test statistic to compare conditional hazard functions which does not require the assumption of proportional hazards, a parametric specification of the relative risk function or randomization of group assignment. Simulation results indicate that the performance of this statistic is satisfactory. The methodology is demonstrated on a data set in prostate cancer.     

财经院校统计学双语教学实践研究

本文通过对统计学双语教学实践的研究和探讨,指出在高等财经院校开展统计学双语,有利于培养符合经济发展的高素质人才,并有助于统计学本学科自身的发展。结合教学的实践,提出了统计学双语教学的实践方法。     

中国经济高速增长及国际贡献的讨论

改革开放以来中国经济持续快速增长,创造了震惊世界的"中国奇迹"。近年来海外某些媒体对中国统计数字的真实性表示怀疑,进而怀疑中国经济增长是一场数字游戏。这显然有失公允。即使离开官方的统计数据也能证明中国二十多年的经济增长是客观存在的,而且中国各级统计部门为减少统计失真也做出了努力。根据国际权威机构的排名,中国国际竞争力明显提升,中国经济的发展对世界经济缓慢复苏具有强力的推动作用。     

ANCILLARITY AND MINIMAL SUFFICIENCY

A discussion of ancillarity in multinomial distributions provides the context for an example which queries the priority of the Sufficiency Principle.     

ASYMPTOTIC TESTS FOR GROWTH CURVE MODELS WITH AUTOREGRESSIVE ERRORS

This paper presents the limit distribution (as the number of time points increase) for the score vector of a growth curve model assuming both stationary and explosive autoregressive (A.R.) errors. Limit distributions of the score statistic and the likelihood-ratio statistic for testing composite hypotheses about the regression parameters of several growth curves, when the autocorrelation parameters are treated as nuisance parameters, are presented.     

Power comparisons of single-sample winsorized T,trimmed T and student's T

The objective of this study is to evaluate the performance of Winsorized t which was proposed to be used as one alternative to the student's t statistic when the parent population is non-normal. The criterion we use is the power of the test. Comparative performance of the Winsorized t, trimmed t, and Studen$tCs t for normal populations and for Studen$tCs t distribution with 8, 4, and 2 degrees of freedom was investigated.     

Power and sample size calculation for paired right-censored data based on survival copula models

Sample size determination is essential during the planning phases of clinical trials. To calculate the required sample size for paired right-censored data, the structure of the within-paired correlations needs to be pre-specified. In this article, we consider using popular parametric copula models, including the Clayton, Gumbel, or Frank families, to model the distribution of joint survival times. Under each copula model, we derive a sample size formula based on the testing framework for rank-based tests and non-rank-based tests (i.e., logrank test and Kaplan–Meier statistic, respectively). We also investigate how the power or the sample size was affected by the choice of testing methods and copula model under different alternative hypotheses. In addition to this, we examine the impacts of paired-correlations, accrual times, follow-up times, and the loss to follow-up rates on sample size estimation. Finally, two real-world studies are used to illustrate our method and R code is available to the user.