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.     

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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.     

舍入数据下Lomax分布形状参数的经验Bayes检验问题

本文研究了舍入数据下Lomax分布形状参数的经验Bayes (EB)单侧检验问题.利用密度函数的递归核估计构造了参数的EB检验函数,并在适当的条件下证明了所提出的EB检验函数的渐近最优性,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.     

平衡设计多向分类多元重复测量模型的Wilks检验

对平衡设计多向分类多元重复测量模型,利用极大似然比方法,推导了对各单个固定效应分别进行检验的Wilks型检验规则.并推导了对多个固定效应进行同时检验的检验规则.推导了非中心分布的参数与原始参数和样本容量的关系.     

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)).     

Application of a Heuristic Scheduling Rule for a Tyre Testing Department

This paper presents the development and application of a heuristic scheduling rule for the Testing Department of a tyre and rubber company. The company had set down five major objectives for this department: (1) minimize average flow-time; (2) maintain an average flow-time of less than two weeks; (3) maintain pre-set maximum individual test flow times; (4) assign a higher priority to important jobs; (5) maintain a balanced mix between truck and passenger tyres. A composite scheduling rule was designed to meet these objectives which contains the following four components: (a) slack-time per remaining operation; (b) shortest processing time; (c) an urgency factor to prioritize important jobs; (d) a balancing factor to enable a desired proportion between truck and passenger tyres. This composite rule is tested against the existing scheduling scheme used by the foreman and against the simple S.P.T. rule. The results are very encouraging.     

Towards data driven selection of a penalty function for data driven Neyman tests

The data driven Neyman statistic consists of two elements: a score statistic in a finite dimensional submodel and a selection rule to determine the best fitted submodel. For instance, Schwarz BIC and Akaike AIC rules are often applied in such constructions. For moderate sample sizes AIC is sensitive in detecting complex models, while BIC works well for relatively simple structures. When the sample size is moderate, the choice of selection rule for determining a best fitted model from a number of models has a substantial influence on the power of the related data driven Neyman test. This paper proposes a new solution, in which the type of penalty (AIC or BIC) is chosen on the basis of the data. The resulting refined data driven test combines the advantages of these two selection rules.     

随机效应模型中方差分量的经验Bayes检验问题

给出了双向分类随机效应模型中方差分量的Bayes检验的判决函数,利用核估计的方法,构造了相应的经验Bayes(EB)检验的判决函数．在适当的条件下证明了EB判决函数是渐近最优的且有收敛速度．给出了模型的特例和推广．最后,举出一个满足定理条件的例子．     

几何分布时间序贯检验的贝叶斯推断

设有统计模型｛ｘ,Ｂｘ,Ｐθ｝,θ∈（０,１）,其中Ｐθ为几何分布：Ｐθ（Ｘ＝ｋ）＝（１－θ）θ＾ｋ－１ｋ＝１,２,…。考虑检验问题：θ＝θｏ ｖｓ． θ＝θ１（０〈θ０〈θ１〈１）本文对一种依次试验的时间序贯样本,给出了上述检验问题的贝叶斯停止判决法则,其中损失函数为试验费用和误判损失之和,贝叶斯停止判决法则由后验概率的两组界（上界和下界）所给出。     

Information theory and alternate-hypothesis tests

Lindley's concept of the information in an experiment is used to study alternate-hypothesis statistical tests. A test is considered to be a combination of a sampling rule and a decision rule which is based on the samples taken. Two information functions are formed: the information about the unknown parameter contained in the samples taken and the information contained in the decision to which these samples lead. The former quantity is at least as great as the latter, with equality if, and only if, the test is an SPRT (sequential probability ratio test) neglecting the excess over the boundary.If the average information contained in the samples taken in one alternate-hypothesis test is greater than the average information contained in the samples taken in another, then the first test has greater expected length. It is also shown that an SPRT neglecting excess is the unique alternate-hypothesis test that utilizes all the information contained in the samples taken. These results provide a proof of the standard result concerning the optimal character of the SPRT neglecting excess.The major purpose of this paper is to develop an information-theoretic analysis of alternate-hypothesis tests. By means of this approach, new results on the SPRT and alternate-hypothesis tests in general are obtained.     

Comparison of Heuristic Criteria for Fuzzy Rule Selection in Classification Problems

This paper compares heuristic criteria used for extracting a pre-specified number of fuzzy classification rules from numerical data. We examine the performance of each heuristic criterion through computational experiments on well-known test problems. Experimental results show that better results are obtained from composite criteria of confidence and support measures than their individual use. It is also shown that genetic algorithm-based rule selection can improve the classification ability of extracted fuzzy rules by searching for good rule combinations. This observation suggests the importance of taking into account the combinatorial effect of fuzzy rules (i.e., the interaction among them).     

基于非关联流动法则的圆形隧洞滑移线解答

圆形隧洞的滑移线方程早在20世纪中期苏联学者已给出解答,并在国内广泛应用．推导过程中假设滑移线与速度矢量方向夹角为45°-φ/2是错误的,理论上滑移线与速度矢量方向的夹角与采用的流动法则有关．该文分别从关联流动法则和非关联流动法则出发,推导了圆形隧洞的滑移线方程和水平方向破裂体深度的表达式．通过与模型试验结果进行对比,对岩土材料应采用基于非关联流动法则的滑移线解答.     

Trapezoidal and midpoint splittings for initial-boundary value problems

In this paper we consider various multi-component splittings based on the trapezoidal rule and the implicit midpoint rule. It will be shown that an important requirement on such methods is internal stability. The methods will be applied to initial-boundary value problems. Along with a theoretical analysis, some numerical test results will be presented.

     

Analysis of interpretability-accuracy tradeoff of fuzzy systems by multiobjective fuzzy genetics-based machine learning

This paper examines the interpretability-accuracy tradeoff in fuzzy rule-based classifiers using a multiobjective fuzzy genetics-based machine learning (GBML) algorithm. Our GBML algorithm is a hybrid version of Michigan and Pittsburgh approaches, which is implemented in the framework of evolutionary multiobjective optimization (EMO). Each fuzzy rule is represented by its antecedent fuzzy sets as an integer string of fixed length. Each fuzzy rule-based classifier, which is a set of fuzzy rules, is represented as a concatenated integer string of variable length. Our GBML algorithm simultaneously maximizes the accuracy of rule sets and minimizes their complexity. The accuracy is measured by the number of correctly classified training patterns while the complexity is measured by the number of fuzzy rules and/or the total number of antecedent conditions of fuzzy rules. We examine the interpretability-accuracy tradeoff for training patterns through computational experiments on some benchmark data sets. A clear tradeoff structure is visualized for each data set. We also examine the interpretability-accuracy tradeoff for test patterns. Due to the overfitting to training patterns, a clear tradeoff structure is not always obtained in computational experiments for test patterns.     

基于纵向数据下连续型单参数指数族参数的经验贝叶斯检验(英文)

本文讨论了在纵向数据下,运用非参数估计方法构造了连续型单参数指数族参数的经验贝叶斯检验函数,证明了所提出的经验贝叶斯检验函数的渐近最优性,并获得了它的收敛速度.     

基于Fisher准则的两组判别分析方法在夏季降水预报中的应用

根据1959—2004年(12月到次年3月)逐月大气环流特征量资料,运用基于F isher准则的两组判别分析方法,预报安徽宣城夏季降水量.结果表明,历史拟合率高,在预报业务中具有实用价值.     

Semi-discrete Galerkin approximation of the single layer equation by general splines

This paper deals with a semi-discrete version of the Galerkin method for the single-layer equation in a plane, in which the outer integral is approximated by a quadrature rule. A feature of the analysis is that it does not require high precision quadrature or exceptional smoothness of the data. Instead, the assumptions on the quadrature rule are that constant functions are integrated exactly, that the rule is based on sufficiently many quadrature points to resolve the approximation space, and that the Peano constant of the rule is sufficiently small. It is then shown that the semi-discrete Galerkin approximation is well posed. For the trial and test spaces we consider quite general piecewise polynomials on quasi-uniform meshes, ranging from discontinuous piecewise polynomials to smoothest splines. Since it is not assumed that the quadrature rule integrates products of basis functions exactly, one might expect degradation in the rate of convergence. To the contrary, it is shown that the semi-discrete Galerkin approximation will converge at the same rate as the corresponding Galerkin approximation in the and norms. Received March 15, 1996 / Revised version received June 2, 1997     

An efficient algorithm for range computation of polynomials using the Bernstein form

We present a novel optimization algorithm for computing the ranges of multivariate polynomials using the Bernstein polynomial approach. The proposed algorithm incorporates four accelerating devices, namely the cut-off test, the simplified vertex test, the monotonicity test, and the concavity test, and also possess many new features, such as, the generalized matrix method for Bernstein coefficient computation, a new subdivision direction selection rule and a new subdivision point selection rule. The features and capabilities of the proposed algorithm are compared with those of other optimization techniques: interval global optimization, the filled function method, a global optimization method for imprecise problems, and a hybrid approach combining simulated annealing, tabu search and a descent method. The superiority of the proposed method over the latter methods is illustrated by numerical experiments and qualitative comparisons.     

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.     

一类二阶延迟微分方程梯形方法的延迟依赖稳定性分析

本文涉及一类二阶延迟微分方程数值方法的稳定性研究．通过运用边界轨迹法,分析了梯形方法的延迟依赖稳定区域并找到其准确边界．随后建立了解析和数值稳定区域的联系并从理论上证明了梯形方法能完全保持模型问题的延迟依赖稳定性．