A permutation approach for testing heterogeneity in two-sample categorical variables

In many sciences researchers often meet the problem of establishing if the distribution of a categorical variable is more concentrated, or less heterogeneous, in population P ₁ than in population P ₂. An approximate nonparametric solution to this problem is discussed within the permutation context. Such a solution has similarities to that of testing for stochastic dominance, that is, of testing under order restrictions, for ordered categorical variables. Main properties of given solution and a Monte Carlo simulation in order to evaluate its degree of approximation and its power behaviour are examined. Two application examples are also discussed.     

A new alternative to the standard F test for clustered data

The data collection process and the inherent population structure are the main causes for clustered data. The observations in a given cluster are correlated, and the magnitude of such correlation is often measured by the intra-cluster correlation coefficient. The intra-cluster correlation can lead to an inflated size of the standard F test in a linear model. In this paper, we propose a solution to this problem. Unlike previous adjustments, our method does not require estimation of the intra-class correlation, which is problematic especially when the number of clusters is small. Our simulation results show that the new method outperforms the existing methods.     

Confidence interval estimation of the difference between paired AUCs based on combined biomarkers

In many diagnostic studies, multiple diagnostic tests are performed on each subject or multiple disease markers are available. Commonly, the information should be combined to improve the diagnostic accuracy. We consider the problem of comparing the discriminatory abilities between two groups of biomarkers. Specifically, this article focuses on confidence interval estimation of the difference between paired AUCs based on optimally combined markers under the assumption of multivariate normality. Simulation studies demonstrate that the proposed generalized variable approach provides confidence intervals with satisfying coverage probabilities at finite sample sizes. The proposed method can also easily provide P-values for hypothesis testing. Application to analysis of a subset of data from a study on coronary heart disease illustrates the utility of the method in practice.     

Modified inference about the mean of the exponential distribution using moving extreme ranked set sampling

The maximum likelihood estimator (MLE) and the likelihood ratio test (LRT) will be considered for making inference about the scale parameter of the exponential distribution in case of moving extreme ranked set sampling (MERSS). The MLE and LRT can not be written in closed form. Therefore, a modification of the MLE using the technique suggested by Maharota and Nanda (Biometrika 61:601–606, 1974) will be considered and this modified estimator will be used to modify the LRT to get a test in closed form for testing a simple hypothesis against one sided alternatives. The same idea will be used to modify the most powerful test (MPT) for testing a simple hypothesis versus a simple hypothesis to get a test in closed form for testing a simple hypothesis against one sided alternatives. Then it appears that the modified estimator is a good competitor of the MLE and the modified tests are good competitors of the LRT using MERSS and simple random sampling (SRS).     

Least-square regularized regression with non-iid sampling

We study the least-square regression learning algorithm generated by regularization schemes in reproducing kernel Hilbert spaces. A non-iid setting is considered: the sequence of probability measures for sampling is not identical and the sampling may be dependent. When the sequence of marginal distributions for sampling converges exponentially fast in the dual of a Hölder space and the sampling process satisfies a polynomial strong mixing condition, we derive learning rates for the learning algorithm.     

中国制造业集聚：度量与显著性检验 ——基于集聚测量新方法

 根据集聚分布的自身特点,本文提出了可以进行显著性检验的普适性产业集聚指数,并对我国28制造业1994年的860个样本和2004年800个样本进行测量和检验,我们的计算结果表明,中国制造业的集聚强度总体上呈上升趋势,同时具有集聚和扩散双重现象。     

缺失数据时两个对数正态分布的估计和检验

讨论了部分缺失数据两对敷正态分布的参数估计和关于总体相同的似然比检验.证明了估计的强相合性和渐近正态性,给出了似然比检验统计量的极限分布,并讨论基于精确分布的检验问题.     

股指期货价格发现功能研究——基于印度Nifty50股指期货的实证分析

使用基于VECM（向量误差修正模型）的Granger因果关系检验法验证了印度Nifty50股指期货与现货市场之间的价格发现过程,结果发现现货市场在价格发现过程中占主导地位,在新兴市场国家的印度,股指期货缺乏价格发现功能。     

解读敦煌吐蕃文书P.T.1286号写卷及其历史内容

P.T.1286号写卷颇具学术研究价值.文章对写卷中的重要字、关键词及其句法作了新的认真解读,通过字斟句酌,发现了一些被前人忽视的历史问题,提出了独到见解,进而重新将藏文原文译为汉文;并以P.T.1286号写卷藏文原文内容为主、其他相关史料为辅,探讨西藏远古史上的有关疑难问题,重点解析聂赤赞普之前部落林立时代卫藏等地著名部族的分布概况、大致方位,以及当时雪域高原社会的基本形态、政权模式、部落结构、部族关系等.     

论科技英语中wh-words引导名词性从句的用法

科技英语因其自身特点而成为一个独特的语域.wh-words可以用来引导各种从句.文章研究在科技英语中wh-words的句法功能,重点研究wh-words引导名词性从句的用法.运用统计学方法对wh-words在科技英语(T4)等语料库中的分布频率进行分析,根据卡方测试的结果做出判断.wh-words在不同的从句中使用的频率高低不等,有些wh-words在不同的语料库中的分布存在着显著不同,如what等词.因此,在某种程度上可以说,wh-words这一组词的用法特点是科技英语区别于普通英语的一个反映.