关于区间估计与假设检验的最优性

本首先提出了区间估计的另一个优良性因素－虚假度，进而建立或疏理了区间估计与假设检验的优良性、最优性概念，然后给出了区间估计与假设检验最优性的关系。     

有关假设检验的问题

针对假设检验教学引发的问题．阐述了Neyman-Pearson假设检验和置信区间的对偶性．给出用假设检验确定置信区间的做法．     

假设检验中的三个问题及其思考

讨论了假设检验的教学和实践中可能会遇到的三个问题.分析得到三个结论:理论上原假设与备择假设可以任意设定,但实践中常常按照某种规则来设定;假设检验的两个常用的决策准则中,p值准则更精确,为决策者接受原假设提供了一个"风险"的度量;假设检验理论在处理利用样本推断总体的问题中是普遍有效的.     

假设检验的稳定性

讨论了假设检验的稳定性.与通常的基于Neyman-Pearson引理的检验不同,考虑了所谓检验的遗憾与纠错.当观察到X₁,X₂,…,X_n,可以此对假设作出判决,如果此时又得到一个新的数据X_n+1,可以用同样的方法,基于X₁,X₂,…,X_n,X_n+1再进行假设检验.可能面临3种情形:(ⅰ)原判决是对的,新判决却是错的;(ⅱ)原判决是错的,新判决却是对的;(ⅲ)原判决与新判决同时都对或同时都错.希望出现(ⅰ)的概率尽可能地小而出现(ⅱ)的概率尽可能地大.在实践中,当第1类错误α和第2类错误β确定后,样本大小的选取多少有些任意性(通常会略大些),因此这一问题似乎更应考虑.还给出了一些最优计划.     

有界区间假设检验问题的研究

提出原假设为一个有界区间时的假设检验问题,给出了求这种假设检验的接受域、拒绝域、犯两类错误概率的方法,举例说明了这种假设检验在实际中的广泛应用.     

信号检测问题的假设检验:大偏差方法

本文研究了马氏链的假设检验问题.利用大偏差的方法,获得了马氏链的假设检验的否定域,并得到了犯第二二类错误概率的衰减性.     

奇异线性模型的线性假设检验问题

本文研究奇异线性模型的假设检验问题，我们用初等直接的方法了根据最小二乘统一理论所构造的检验统计量服从Ｆ分布，并给出了这些结果在ｐａｎｅｌ数据模型。两级抽样回归模型以及不完全数据回归模型中的应用。     

假设检验中的原假设选取问题

针对假设检验中"对一个具体问题,在相同的显著性水平α下进行假设检验,如果交换原假设和备择假设,得到的结论可能完全相反"这一问题进行分析,并给出根据实际问题的要求如何正确建立原假设和备择假设的方法.     

二维正态分布的假设检验

将二维随机向量分解成互不相关的主成分,通过对两主成分的正态独立性检验达到二维随机向量正态性检验的目的.     

混料模型的假设检验

假设检验是统计学中根据一定假设条件由样本推断总体的一种方法。假设检验在经济和社会生活的各个领域中得到了极为广泛的应用,尤其在医学方面有着更为广阔的应用前景。本文以假设检验为基础,主要研究线性混料模型中的假设检验问题。     

Testing the Gumbel Hypothesis Via the Pot-Method

We consider an i.i.d. sample, generated by some distribution function, which belongs to the domain of attraction of an extreme value distribution with unknown shape and scale parameters. We treat the scale parameter as a nuisance parameter and establish for the hypothesis of Gumbel domain of attraction an asymptotically optimal test based on those observations among the sample, which exceed a given threshold sequence. Asymptotic optimality is achieved along certain contiguous extreme value alternatives within the concept of local asymptotic normality (LAN). Adaptive test procedures exist under restrictive assumptions. The finite sample size behavior of the proposed test is studied by simulations and it is compared to that of a test based on the sample coefficient of variation.     

A Statistic for Testing the Null Hypothesis of Elliptical Symmetry

We present and study a procedure for testing the null hypothesis of multivariate elliptical symmetry. The procedure is based on the averages of some spherical harmonics over the projections of the scaled residual (1978, N. J. H. Small, Biometrika65, 657–658) of the d-dimensional data on the unit sphere of ^d. We find, under mild hypothesis, the limiting null distribution of the statistic presented, showing that, for an appropriate choice of the spherical harmonics included in the statistic, this distribution does not depend on the parameters that characterize the underlying elliptically symmetric law. We describe a bivariate simulation study that shows that the finite sample quantiles of our statistic converge fairly rapidly, with sample size, to the theoretical limiting quantiles and that our procedure enjoys good power against several alternatives.     

基于可信度的统计检验决策研究

针对单侧假设检验中出现的自相矛盾的决策问题,在简单分析单侧假设检验两类错误具体形式的基础上,给出了检验决策可信度的概念及其计算公式,并提出了用决策可信度来判断单侧检验原假设及其结论是否有效的方法.实例表明,方法是可行的,有效的.     

条件极值两种求法的不等价条件分析

本文说明了求条件极值的代入法与Ｌａｇｒａｎｇｅ乘数法的条件不等价。分析了这种不等价的原因，得到了求一般条件极值时，代入法与Ｌａｇｒａｎｇｅ乘数法均有效、前者无效而后者有效，以及两种方法均无效的各种不同条件．     

一种求解救灾物资运输问题的改进解法

本文通过对B运输问题建立数学模型,提出了一种求解B运输问题的改进解法。改进解法首先通过最小元素法求出初始解,然后进行变量闭回路法调整,直到求出最优解,并给出了一个计算实例证明了解法的有效性。文章还对改进解法和另外两种现有的算法进行了综合的分析,由于改进解法计算过程中采用的变量闭回路法省略了求检验数的环节,使得新算法比两种现有的算法更简便。     

A Semiparametric Two-Sample Hypothesis Testing Problem for Random Graphs

Two-sample hypothesis testing for random graphs arises naturally in neuroscience, social networks, and machine learning. In this article, we consider a semiparametric problem of two-sample hypothesis testing for a class of latent position random graphs. We formulate a notion of consistency in this context and propose a valid test for the hypothesis that two finite-dimensional random dot product graphs on a common vertex set have the same generating latent positions or have generating latent positions that are scaled or diagonal transformations of one another. Our test statistic is a function of a spectral decomposition of the adjacency matrix for each graph and our test procedure is consistent across a broad range of alternatives. We apply our test procedure to real biological data: in a test-retest dataset of neural connectome graphs, we are able to distinguish between scans from different subjects; and in the C. elegans connectome, we are able to distinguish between chemical and electrical networks. The latter example is a concrete demonstration that our test can have power even for small-sample sizes. We conclude by discussing the relationship between our test procedure and generalized likelihood ratio tests. Supplementary materials for this article are available online.     

重复测量中两组均值是否相等的假设检验

在重复测量中，存在着检验两个随机试验组的各次测量均值是否相等的问题。假定所有试验对象初始值无差异，那么只需对所有均值的对比进行检验就可以了；若这个假定不成立，则需要直接检验各次测量的均值是否均对应相等。在正态性、复合对称的假定下，前一种情形已有很好的结论。针对后面一种情况，本文采用似然比检验的方法进行了检验，构造出了检验统计量的分布函数表。并对计算的精确度作了较好的控制。