对正态总体方差的一致最优势无偏检验

本文给出了对正态总体方差的一致最优势无偏（ＵＭＰＵ）检验的临界值。     

线性混合模型中固定效应和方差分量同时最优估计

对于具有广泛应用的含有两个方差分量的线性混合模型, 找到了一组简单条件. 在这些条件下, 证明了固定效应的最小二乘估计和方差分量的方差分析估计同时是最小方差无偏估计; 获得了固定效应的精确置信区间和随机效应的方差分量的一致最优无偏检验; 得到了随机效应方差的方差分析估计取负值的概率精确表达式.     

Cp值的最优无偏估计及最优检验

本文利用统计推断原理,对于正态分布、指数分布及Gamma分布的情形,导出工序能力指致C_(?)值的最优无偏估计及最优检验,还讨论了两个质量指标情形的工序能力指数的估计。     

线性混合模型方差分量的检验

对于含有两个方差分量的随机效应设计阵为任意阵的线性混合模型的方差分量单边检验问题给出了精确的F检验和基于广义p值的检验.对于给出的精确的F检验给出检验存在条件以及是一致最优无偏检验的条件.通过数值模拟,基于广义p值检验的功效和犯第一类错误的概率被讨论,由模拟结果可以看出基于广义p值的检验很好地控制了犯第一类错误的概率.     

基于样本空间中序关系构造参数置信限方法的一个注记

，证明了存在样本空间中的一种序关系，使得基于这种序关系构造的参数的上（下）置信限是同一置信水平下的一致最精确上（下）置信限．本文还证明了，多参数指数族中一个参数的一致最精确无偏上（下）置信限也能基于样本空间中的一种序关系构造出来。     

非线性参数规划问题ε-最优解集集值映射的连续性

对非线性参数规划问题ε-最优解集集值映射的连续性条件进行了研究.首先在可行集集值映射局部有界且正则的条件下,讨论了非线性参数规划问题最优值函数的连续性,然后针对ε-最优解集集值映射的结构特征并利用此结果和集值分析理论,给出了非线性参数规划问题ε-最优解集集值映射连续的一个充分条件.     

NA样本下回归函数估计的收敛速度

在误差为NA序列的条件下,研究了固定设计点列情形下非参数回归函数一般权函数的非参数估计,并在一些基本条件下给出了估计的一致最优强收敛速度.     

非线性参数规划问题$\varepsilon$-最优解集集值映射的连续性

对非线性参数规划问题$\varepsilon$-最优解集集值映射的连续性条件进行了研究.首先在可行集集值映射局部有界且正则的条件下,讨论了非线性参数规划问题最优值函数的连续性,然后针对$\varepsilon$-最优解集集值映射的结构特征并利用此结果和集值分析理论,给出了非线性参数规划问题$\varepsilon$-最优解集集值映射连续的一个充分条件.     

误差为鞅差序列的回归函数估计的收敛速度

当误差为鞅差序列时,研究了固定设计点列情形下非参数回归函数一般权函数的非参数估计,并在一些基本条件下给出了估计的一致最优强收敛速度.     

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.     

单向分类随机效应模型的异常值检测

本文研究平衡的单向分类随机效应模型中单个异常值的检验问题,在随机效应的异常均值滑动模型下,导出异常值的检验统计量及其精确分析,并证明了该检验的一致最优无偏性,另外,对于误差变量的异常均值滑动模型,提出了一个近似的检验过程,并运用随机模拟给出该检验的临界值表,最后,对一组模拟数据进行说明。     

连续型单参指数族参数的经验Bayes检验问题:NA样本情形

本文对连续型单参指数族单边和双边假设检验问题导出了Bayes检验函数 ,利用同分布NA样本构造了经验Bayes(EB)检验函数 ,在适当条件下证明了EB检验函数的渐近最优性并获得了其收敛速度 ,文末给出一个满足定理条件的例子     

负相伴样本情形线性指数分布参数的经验Bayes双侧检验问题

本文讨论了负相伴样本情形线性指数分布参数的经验Bayer(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,最后,给出一个有关本文主要结果的例子。     

伽玛分布族参数的经验Bayes双边检验的收敛速度:NA样本情形

本文研究了NA样本情形下,伽玛分布族形状参数的经验Bayes(EB)双边检验问题.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,并在适当的条件下,证明了所提出的经验Bayes检验函数的渐近取优(a.o.)性,获得了其收敛速度.     

A characterization of the one parameter exponential family of distributions by monotonicity of likelihood ratios

Summary Any one parameter exponential family of distributions has monotone likelihood ratios. As the product probabilities of n identical distributions of an exponential family form again an exponential family, it has monotone likelihood ratios for arbitrary n. Furthermore, the members of an exponential family are mutually absolutely continuous. In Part 1, we show that these properties uniquely characterize the exponential family. The application of this result to the theory of testing hypotheses (Part 2) shows that if a family of mutually absolutely continuous distributions has uniformly most powerful tests for arbitrary levels of significance, and arbitrary sample sizes, then it is necessarily an exponential family.The research was done while this author was a Visiting Professor in the Department of Statistics at the University of Chicago. It was supported by Research Grants Nos. NSF-G10368 and NSF-G21058 from the Division of Mathematical, Physical and Engineering Sciences of the National Science Foundation.     

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In this paper, the empirical Bayes (EB) two-sided test for parameter of Cox models is investigated under square loss functions. At first by using recursive kernel estimation of probability function the empirical Bayes two-sided test rule is constructed. It proves that the proposed empirical Bayes test rule is asymptotic optimal and convergence rates are obtained under suitable conditions. Finally an example of satisfying theorem conditions is given.     

Curved exponential families of stochastic processes and their envelope families

Exponential families of stochastic processes are usually curved. The full exponential families generated by the finite sample exponential families are called the envelope families to emphasize that their interpretation as stochastic process models is not straightforward. A general result on how to calculate the envelope families is given, and the interpretation of these families as stochastic process models is considered. For Markov processes rather explicit answers are given. Three examples are considered some in detail: Gaussian autoregressions, the pure birth process and the Ornstein-Uhlenbeck process. Finally, a goodness-of-fit test for censored data is discussed.     

Test for a specified signal when the noise covariance matrix is unknown

In the univariate case it is well known that the one sided t test is uniformly most powerful for the null hypothesis against all one sided alternatives. Such a property does not easily extend to the multivariate case. In this paper, a test derived for the hypothesis that the mean of a vector random variable is zero against specified alternatives, when the covariance matrix is unknown. This test depends on the given alternatives and is more powerful than Hotelling's T². The results are derived both for real and complex vector observations and under normal and spherical distributions. The properties of the proposed tests are investigated in detail when a single alternative is specified.     

Third-order efficiency of conditional tests in exponential models: The lattice case

As is well known, in full rank multivariate exponential families, tests of Neyman structure are uniformly most powerful unbiased for one-sided problems. For the case of lattice distributions, the power of these tests—evaluated at contiguous alternatives—is approximated by asymptotic expansions up to errors of order o(n^?1). Surprisingly the tests with Neyman structure are not third-order efficient in the class of all asymptotically similar tests unless the problem is univariate.