细胞自动机组合伪随机序列发生器

结合细胞自动机所特有的单元结构的简单性、单元之间作用的局部性和信息处理的高度并行性等特点,利用细胞自动机产生高速序列;分析了比特与、或、异或运算周期特性,其周期等于各自周期的最小公倍数;证明了比特异或运算的频率特性优于原有的频率特性。研究了比特组合运算的线性复杂独特性,比特与、或运算的线性复杂度等于各自线性复杂度的乘积,异或运算的线性复杂度等于各自线性复杂度之和。利用伪随机特性检测方法和线性复杂度的测试方法的计算机模拟表明细胞自动机组合伪随机序列发生器实现简单、速度高、能有效增加序列周期长,改善序列伪随机统计特性,并能有效增加伪随机序列的线性复杂度。     

Laplace approximations for censored linear regression models

We consider approximate Bayesian inference about scalar parameters of linear regression models with possible censoring. A second-order expansion of their Laplace posterior is seen to have a simple and intuitive form for logconcave error densities with nondecreasing hazard functions. The accuracy of the approximations is assessed for normal and Gumbel errors when the number of regressors increases with sample size. Perturbations of the prior and the likelihood are seen to be easily accommodated within our framework. Links with the work of DiCiccio et al. (1990) and Viveros and Sprott (1987) extend the applicability of our results to conditional frequentist inference based on likelihood-ratio statistics.     

Robustness measures for Bayes linear analyses

We consider the role of global robustness measures in Bayes linear analysis. We suggest two such measures, one for expectation comparisons and one for variance comparisons. Geometric interpretations of the measures are presented. The approach is illustrated by considering the robustness of certain multiplicative models to assumptions of independence, with particular application to a problem arising in an asset management model for water resources.     

线性分式曲线拟合及分段函数在机械设计中的应用

提出对函数曲线很陡情况下的型值点进行线性分式曲线拟合的方法，并给出分段函数曲线拟合在实际问题中的应用示例．     

Sequential estimation in two-truncation parameter family of distributions under type II censoring

Summary This paper deals with the sequential estimation ofq(ϑ₁, ϑ₂) when the underlying density function is of the formf(x)=q(ϑ₁, ϑ₂)h(x), where ϑ₁ and ϑ₂ are unknown truncation parameters. We study the sequential properties of the stopping rule and the sequential estimator ofq(ϑ₁, ϑ₂). In this study we assume that the sample is type II censored.     

用伴随网络法系统求解网络多端口一阶灵敏度

本文提出用稀疏表格法建立多端口伴随同络混合参数矩阵,进而构成了一个有规则的矩阵关系式,可一次求取计算网络多端口一阶灵敏度所需要的支路变量信息。在此基础上,定义了一个矩阵循环乘概念,并给出了系统求解网络多端口输出相对每个元件参数变化的一阶灵敏度公式。     

Robust modelling for asset management

We argue that the principal way to achieve robustness is through good elicitation. This means asking the right questions, to elicit only those judgements which can be given reliably. Bayes linear methods minimise the number of prior judgements employed in the analysis. Only first- and second-order moments are specified. We present an example in which a complex problem is modelled in terms of a relatively small number of meaningful prior judgements. Sensitivity to variations in those judgements is explored here and in Goldstein and Wooff (1992).     

Robust Tests in Semiparametric Partly Linear Models

Abstract.  This paper focuses on the problem of testing the null hypothesis that the regression parameter equals a fixed value under a semiparametric partly linear regression model by using a three-step robust estimate for the regression parameter and the regression function. Two families of tests statistics are considered and their asymptotic distributions are studied under the null hypothesis and under contiguous alternatives. A Monte Carlo study is performed to compare the finite sample behaviour of the proposed tests with the classical one.     

生理药物动力学模型参数的计算

在建立的哺乳动物整体生理药物动力学模型的基础上,导出了生理药物动力学模型参数分配系数(R)、排泄速率常数(KE)及代谢速率常数(KM)的计算公式,并用此公式计算了大鼠静脉注射环孢菌素A后药物在各器官或组织中的动力学分配系数.     

Modelling spatial intensity for replicated inhomogeneous point patterns in brain imaging

Summary.  Pharmacological experiments in brain microscopy study patterns of cellular activ- ation in response to psychotropic drugs for connected neuroanatomic regions. A typical ex- perimental design produces replicated point patterns having highly complex spatial variability. Modelling this variability hierarchically can enhance the inference for comparing treatments. We propose a semiparametric formulation that combines the robustness of a nonparametric kernel method with the efficiency of likelihood-based parameter estimation. In the convenient framework of a generalized linear mixed model, we decompose pattern variation by kriging the intensities of a hierarchically heterogeneous spatial point process. This approximation entails discretizing the inhomogeneous Poisson likelihood by Voronoi tiling of augmented point patterns. The resulting intensity-weighted log-linear model accommodates spatial smoothing through a reduced rank penalized linear spline. To correct for anatomic distortion between subjects, we interpolate point locations via an isomorphic mapping so that smoothing occurs relative to common neuroanatomical atlas co-ordinates. We propose a criterion for choosing the degree and spatial locale of smoothing based on truncating the ordered set of smoothing covariates to minimize residual extra-dispersion. Additional spatial covariates, experimental design factors, hierarchical random effects and intensity functions are readily accommodated in the linear predictor, enabling comprehensive analyses of the salient properties underlying replicated point patterns. We illustrate our method through application to data from a novel study of drug effects on neuronal activation patterns in the brain of rats.