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.     

有关控制图误判概率的若干问题(续)

四、为什么两类误判概率 会带有尺码i? 众所周知,利用控制图来判断生产工序是否正常,从数理统计的观点来看,其实质是个假设检验的问题.但假设检验是难免会犯两类错误的,因此在有关的控制图教科书中都会提及这点.但也许是受到传统上假设检验的两类误判概率的影响,在很多教材中都认为使用控制图时,犯第一类错误的概率与犯第二类错误的概率都是固定的常数,并画出了其间的关系.例如[4]的作者认为;“我们采用的管理图,都是所谓‘3σ原则’的管理图。因此犯第一类错误(α)的可能性是很小的,小于0.3%.但是相反,由于α很小时,第二类错误(β)就要增大…     

关于假设检验中显著性水平的选择

假设检验是质量管理和控制中广泛应用的一种统计推断方法.但是,我们知道根据样本提供的信息对总体的参数或分布进行假设检验,可能导致两类错误:第一类错误是如果零假设H0事实上是正确的,而根据样本信息计算的检验统计量却落在否定区,从而我们错误地拒绝了零假设,这叫以真为假;第二类错误是零假设H0事实上是错误的,而根据样本信息计算的检验统计量落在肯定区,从而使我们错误地接受了零假设,这叫以假乱真.以样本平均数的分布为例,假设检验中造成这两类错误的概率大小关系,可通过下图来说明: 图中分布1,分布2的方差σ2是一样的,对样本平均数假…     

定数截尾样本情形指数分布参数的模糊假设检验

以控制犯两类错误的概率为目的。本文在定数截尾样本情形，研究指数分布参数的模糊假设检验问题，并给出了一个数值例子。     

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.     

以功效函数为主线的假设检验教学

本文基于假设检验相关概念的统一,以及初等统计和高等统计学衔接的考虑,提出了以功效函数为主线的教学设计.首先基于证据原理确定拒绝域的形式,计算相应的功效函数;然后用功效函数描述假设检验的两类错误概率;最后根据给定的显著性水平运用功效函数确定拒绝域的具体形式.     

一类单边假设检验问题原假设和备择假设的判别分析

在单边假设检验问题中,原假设和备择假设的地位一般是不对等的,将两者互换就有可能得到矛盾推断.文章既给出了现有的解析方法,也从描述性统计和推断性统计相结合,以及控制第Ⅱ类错误发生概率的角度给出了进一步的思考,合理解析了单边假设检验出现矛盾推断的难题.     

Panel数据模型的参数bootstrap推断

利用广义p值和参数bootstrap方法,研究了Panel数据模型中未知参数的假设检验问题.对于回归系数,基于最小二乘估计和两步估计方法,考虑了非齐次线性假设检验问题.对于方差分量,研究了单边假设检验问题.进而利用Monte Carlo方法进行模拟研究.模拟结果表明,参数bootstrap检验方法既能较好控制犯第一类错误的概率,又具有较高的功效,且大多数情况下优于广义p值检验方法.     

假设检验可达到速率区域的一个内界

本文用信息论的数据压缩方法讨论两个相关信源假设检验问题．定义了复合假设检验的可达速率对，利用多用户信息论信源码定理得到了复合假设检验可达速率区域的一个内界．     

两因素随机效应模型下平均暴露量的检验

通过两因素随机效应模型来研究某一工厂的平均暴露(exposure)水平．利用广义检验变量和广义枢轴量分别给出了相关假设检验问题的广义p-值．证明了由广义p-值所确定的拒绝域的概率在原假设下取上确界等于在原假设和备择假设的公共边界上取上确界,且进一步证明了当参数趋于原假设和备择假设的公共边界的边界时,犯第一类错误的概率趋于名义显著性水平,并在公共边界的内部做了模拟研究．结果表明,用广义p-值的方法来解决此类问题可得到令人满意的结果．     

置信区间与假设检验关系中的一个误区

本文以Kappa系数和两总体率差值检验为例,说明一般的假设检验法与用置信区间作检验是不相同的。其根源在于两者的标准误估计是不同的:与假设检验法不同的是,在求置信区间时,计算标准误时不需要假定H0成立。即零假设被否定后,不应该再用假设检验中的标准误去代替置信区间中的标准误。     

Panel模型中常见估计的比较

本文研究了Panel模型中回归系数常见估计的比较问题,给出了在Pitman准则,协方差阵准则和广义均方误差准则下最小二乘估计,Within估计,Between估计及两步估计之间的优良性比较结果.特别地,本文证明了在Pitman准则下最小二乘估计一致地优于Between估计.     

Parameters Identification as Identification of External Loads

Substitution of traditional problem of parameters identification to a problem of identification of some additional external load to researched system is offered in conditions, when the initial information on researched parameter and about statistical properties of measurements is absent or is insignificant. Such substitution is always possible as the contribution of any parameter can be considered as the contribution of additional external load in the response of researched system. At the given approach the mistake in a choice of structure of mathematical model with identified parameters does not lead to principle errors of the solution. This additional load compensates all errors. Besides it is possible to carry out check of a hypothesis about a constancy of parameter size.The problem was reduced to solution of the integral equation of the first kind with an inexact kernel and inexact right part. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Comparing robust regression lines associated with two dependent groups when there is heteroscedasticity

The paper deals with three approaches to comparing the regression lines corresponding to two dependent groups when using a robust estimator. The focus is on the Theil–Sen estimator with some comments about alternative estimators that might be used. The first approach is to test the global hypothesis that the two groups have equal intercepts and slopes in a manner that allows a heteroscedastic error term. The second approach is to test the hypothesis of equal intercepts, ignoring the slopes, and testing the hypothesis of equal slopes, ignoring the intercepts. The third approach is to test the hypothesis that the regression lines differ at a specified design point. This last goal corresponds to the classic Johnson and Neyman method when dealing with independent groups and when using the ordinary least squares regression estimator. Based on extant studies, there are guesses about how to proceed in a manner that will provide reasonably accurate control over the Type I error probability: Use some type of percentile bootstrap method. (Methods that assume the regression estimator is asymptotically normal were not considered for reasons reviewed in the paper.) But there are no simulation results providing some sense of how well they perform when dealing with a relatively small sample size. Data from the Well Elderly II study are used to illustrate that the choice between the ordinary least squares estimator and the Theil–Sen estimator can make a practical difference.     

Type I Error Probability Spending for Post-Market Drug and Vaccine Safety Surveillance With Poisson Data

Statistical sequential hypothesis testing is meant to analyze cumulative data accruing in time. The methods can be divided in two types, group and continuous sequential approaches, and a question that arises is if one approach suppresses the other in some sense. For Poisson stochastic processes, we prove that continuous sequential analysis is uniformly better than group sequential under a comprehensive class of statistical performance measures. Hence, optimal solutions are in the class of continuous designs. This paper also offers a pioneer study that compares classical Type I error spending functions in terms of expected number of events to signal. This was done for a number of tuning parameters scenarios. The results indicate that a log-exp shape for the Type I error spending function is the best choice in most of the evaluated scenarios.     

On the common mean of several inverse Gaussian distributions based on a higher order likelihood method

An interval estimation method for the common mean of several heterogeneous inverse Gaussian (IG) populations is discussed. The proposed method is based on a higher order likelihood-based procedure. The merits of the proposed method are numerically compared with the signed log-likelihood ratio statistic, two generalized pivot quantities and the simple t-test method with respect to their expected lengths, coverage probabilities and type I errors. Numerical studies show that the coverage probabilities of the proposed method are very accurate and type I errors are close to the nominal level.05 even for very small samples. The methods are also illustrated with two examples.     

Large deviation theorems in the problem of testing two simple hypotheses

Limit theorems on large deviations of the logarithm of the likelihood ratio are proved for the problem of distinguishing two simple hypotheses in the general scheme of statistical experiments under the null hypothesis and under an alternative hypothesis. The theorems obtained are applied to the investigation of a decrease in the probability of errors of the first and second kind for the Neumann-Pearson criterion.Translated from Ukrainskii Matematicheskii Zhurnal, Vol. 47, No. 2, pp. 227–235, February, 1995.     

A Novel Category of Vague Abstracta

Much attention has been given to the question of ontic vagueness, and the issues usually center around whether certain paradigmatically concrete entities - cats, clouds, mountains, etc. - are vague in the sense of having indeterminate spatial boundaries. In this paper, however, I wish to focus on a way in which some abstracta seem to be locationally vague. To begin, I will briefly cover some territory already covered regarding certain types of “traditional” abstracta and the ways they are currently alleged to be vague. I then wish to discuss two types of “nontraditional” abstracta and the sense in which I think some of these objects are locationally vague. I will next reexamine some of the traditional abstracta and discuss whether any of these objects are locationally vague in the novel way suggested for the nontraditional sorts. I’ll finish by discussing objections, and conclude with some remarks about characterizing the abstract/concrete distinction.     

状态转移下的利率预期与误差学习假说

采用线性回归和状态转移模型讨论利率预期的"误差学习假说"和不同期限利率预测误差之间的关联性,并考虑带有风险溢价时预测误差的作用.分析发现,1年期限的"误差学习假说"在银行间国债市场中是成立的,不同期限的利率预期对预测误差的反应呈现一定的独立性,不同的状态中预测误差和风险溢价对利率预期的影响是不同的,考虑风险溢价有助于提升对利率预期变动的解释.     

解抛物型方程的半离散精细积分法

本讨论抛物型方程混合问题的解法．提出在有限元半离散过程后，用精细积分法获得一个较好的解，并且分析了这种方法的误差，证明了用这种方法和二次插值，在节点上有O(h^4)的超收敛性．