完全随机缺失机制下伽玛分布中形状参数的经验贝叶斯推断

在完全随机缺失机制下构造了伽玛分布参数的经验贝叶斯检验函数,并获得了它的渐进最优性.在适当的条件下证明了所提出的经验贝叶斯检验函数收敛速度可任意接近O(n(~(-1/2)).     

随机删失下伽玛分布族参数的经验Bayes双边检验

利用经验贝叶斯方法研究了伽玛分布参数的双边检验问题,构造了一个在历史样本被随机右删失的条件下参数的经验Bayes检验函数,在适当的条件下证明了所提出的经验Bayes检验函数的渐近最优性,并获得了它的收敛速度可任意接近O(n~(-1/2)).     

基于近邻方法下分布参数的贝叶斯分析

本文基于近邻方法下,构造了连续型单参数指数族参数的经验Bayes(EB)检验函数,在适当的条件下证明了所提出的经验Bayes检验函数的大样本性质.     

渐近最优的经验贝叶斯决策

本文在加权线性损失下讨论一类广义指数分布刻度参数的经验贝叶斯检验问题.利用核密度估计函数构造单调的经验贝叶斯检验函数,在适当的条件下证明所构造的检验函数的渐近最优性并获得其收敛速度.该收敛速度可以任意接近O(n-1).最后,给出一个例子用以验证本文的主要结果是合理的.     

广义Pareto分布参数的经验Bayes检验问题

讨论了独立同分布样本情形广义Pareto分布参数的经验Bayes单侧检验问题,利用核密度函数的核估计构造了参数的经验Bayes检验函数,并在一定的条件下,证明了此经验Bayes检验函数的渐近最优性,获得了其收敛速度.     

连续型单参数指数族参数的经验Bayes检验函数的收敛速度

对独立同分布样本情形的连续型单参数指数族的单边假设检验问题,在线性损失下 导出了单调的Bayes检验函数,构造了相应的经验Bayes(EB)检验函数. 在一定条件下, 获得的经验Bayes检验函数的收敛速度可任意接近$O(n^{-1})$.最后给出了满足定理条件的两个例子.     

负相伴样本情形连续型单参数指数族参数的经验Bayes检验问题

在加权"线性损失"下讨论了负相伴样本情形连续型单参数指数族参数的经验Bayes(EB)检验问题.利用概率密度函数的核估计构造了参数的经验Bayes检验函数,并获得了它的渐近最优(a.o.)性,在适当的条件下证明了所提出的经验Bayes检验函数的收敛速度可任意接近O(n-1/2).     

缺失数据下指数威布尔分布族参数的经验Bayes分析

本文讨论了缺失数据下指数威布尔分布族参数的经验Bayes（EB）检验问题,利用概率密度函数的核估计构造了参数的经验Bayes检验函数,并证明了所提出的经验Bayes检验函数的渐近最优（a．o．）性,并获得了它的收敛速度．     

线性指数分布参数的经验Bayes检验问题

分别讨论了线性指数分布参数的经验Bayes(EB)单侧和双侧检验问题.利用概率密度函数的核估计分别构造了参数的经验Bayes检验函数,在适当的条件下证明了所提出的经验Bayes检验函数的渐近最优(a.o.)性并获得了它的收敛速度.最后,给出一个有关主要结果的例子.     

线性指数分布参数的经验Bayes检验问题：φ混合样本情形

讨论了弱平稳φ混合样本下线性指数分布参数的经验Bayes(EB)双侧检验问题,利用概率密度函数的核估计构造了参数的经验Bayes检验函数,在适当的条件下证明了所提出的经验Bayes检验函数的渐近最优(a.o.)性,并获得了它的收敛速度.     

基于ZIP模型的零膨胀检验方法的比较研究

针对已存在关于零膨胀的R检验、C检验、Score检验、卡方检验、似然比检验、Wald检验和基于置信区间检验,通过Monte Carlo模拟分析方法,在功效和犯第一类错误比例的意义下,在不同零膨胀程度、不同均值和不同样本量下对上述检验作比较研究.得到了不同条件下7种检验方法的优良性,并结合理论对7种检验方法进行分析.     

Arguing About the Effectiveness of Assessments for the Classroom

Approaches to test score use and test purpose lack the well-developed methodological guidelines and established sources of evidence available for intended score interpretation. We argue in this paper that this lack fails to reflect the ultimate purpose of a test score—to help solve an important problem faced by intended test users. We explore the treatment of intended test purpose and test score use under the chain of assumption/inferences perspective identified within an argument-based approach to validity. Next, we revisit the notion of test score use and argue that, at least for classroom assessments based on complex constructs, such as learning progressions in math and science, test score use can be more effectively conceptualized as part of a potential solution to solving a problem, or “job-to-be-done.”. We argue for shifting from the definition of validity to the concept of effectiveness. Finally, we illustrate an argument- based approach to test score effectiveness by contrasting effectiveness arguments for interim assessments based on a conventional test blueprint or a test blueprint augmented with learning progressions.     

正项级数的D-C判别法

本文将正项级数的比值审敛法 (达朗贝尔 D' Alembert判别法 )和根值审敛法 (柯西 Cauchy判别法 )结合起来 ,得到正项级数的一个新的审敛法 ,且称之为 D-C判别法 .     

Planning accelerated life tests with random effects of test chambers

In accelerated life tests (ALTs), test units are often tested in multiple test chambers along with different stress conditions. The nonhomogeneity of test chambers precludes the complete randomized experiment and may affect the life‐stress relationship of the test product. The chamber‐to‐chamber variation should be taken into account for ALT planning so as to obtain more accurate test results. In this paper, planning ALTs under a nested experimental design structure with random test chamber effects is studied. First, by a 2‐phase approach, we illustrate to what extent different test chamber assignments to stress conditions may impact the estimation of unknown parameters. Then, D‐optimal test plans with 2 test chambers are considered. To construct the optimal design, we establish the generalized linear mixed model for failure‐time data and apply a quasi‐likelihood method, where test chamber assignments, as well as other decision variables that are required for planning ALTs, are simultaneously determined.     

Empirical likelihood based goodness-of-fit testing for generalized linear mixed models

In this paper, we propose a bias-corrected empirical likelihood （BCEL） ratio to construct a goodness- of-fit test for generalized linear mixed models. BCEL test maintains the advantage of empirical likelihood that is self scale invariant and then does not involve estimating limiting variance of the test statistic to avoid deteri- orating power of test. Furthermore, the bias correction makes the limit to be a process in which every variable is standard chi-squared. This simple structure of the process enables us to construct a Monte Carlo test proce- dure to approximate the null distribution. Thus, it overcomes a problem we encounter when classical empirical likelihood test is used, as it is asymptotically a functional of Gaussian process plus a normal shift function. The complicated covariance function makes it difficult to employ any approximation for the null distribution. The test is omnibus and power study shows that the test can detect local alternatives approaching the null at parametric rate. Simulations are carried out for illustration and for a comparison with existing method.     

Hypothesis tests for high-dimensional covariance structures

We consider hypothesis testing for high-dimensional covariance structures in which the covariance matrix is a (i) scaled identity matrix, (ii) diagonal matrix, or (iii) intraclass covariance matrix. Our purpose is to systematically establish a nonparametric approach for testing the high-dimensional covariance structures (i)–(iii). We produce a new common test statistic for each covariance structure and show that the test statistic is an unbiased estimator of its corresponding test parameter. We prove that the test statistic establishes the asymptotic normality. We propose a new test procedure for (i)–(iii) and evaluate its asymptotic size and power theoretically when both the dimension and sample size increase. We investigate the performance of the proposed test procedure in simulations. As an application of testing the covariance structures, we give a test procedure to identify an eigenvector. Finally, we demonstrate the proposed test procedure by using a microarray data set.

     

有序分组资料的线性趋势检验的确切概率计算

有序分组资料的线性趋势检验主要用于检验几个二项总体反应率的线性趋势；在医学与生物学中最典型的用途是研究剂量和反应的关系． 如果用Ｚ检验来代替通常在本检验中所用的卡方检验，就可容易地导出本检验的确切概率计算方法，和其他２×Ｃ有序列联表有着相同的形式．本文给出了该检验的确切概率计算方法．     

Trimmed portmanteau test for linear processes with infinite variance

In this article, we consider a model check test for linear processes with infinite variance. As a test statistic, we employ the portmanteau test with trimmed residuals. It is shown that the limiting null distribution of the test is a chi-square distribution. Simulation results are provided for illustration.     

Testing for Uniformity of the Residual Life Time Based on Dynamic Kullback-Leibler Information

In this article a goodness of fit test for distributional assumptions regarding the residual lifetime is proposed. The test is based on a Vasicek type sum log-spacings estimators of a dynamic version of Kullback-Leibler information. The specific distributional hypothesis considered is of the uniformity over [0,1]. However, the test can be used for testing any simple goodness of fit hypothesis. The asymptotic distribution of the test statistic together with a tabulation of the critical points for different sample sizes are given. Finally, the power function of the test is empirically studied in comparison with some competitors, and the test appears to be meritorious.     

Near-exact distributions for the independence and sphericity likelihood ratio test statistics

In this paper we show how, based on a decomposition of the likelihood ratio test for sphericity into two independent tests and a suitably developed decomposition of the characteristic function of the logarithm of the likelihood ratio test statistic to test independence in a set of variates, we may obtain extremely well-fitting near-exact distributions for both test statistics. Since both test statistics have the distribution of the product of independent Beta random variables, it is possible to obtain near-exact distributions for both statistics in the form of Generalized Near-Integer Gamma distributions or mixtures of these distributions. For the independence test statistic, numerical studies and comparisons with asymptotic distributions proposed by other authors show the extremely high accuracy of the near-exact distributions developed as approximations to the exact distribution. Concerning the sphericity test statistic, comparisons with formerly developed near-exact distributions show the advantages of these new near-exact distributions.