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．     

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.     

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.     

Strong orthogonal decompositions and non‐linear impulse response functions for infinite‐variance processes

The author proves that Wold‐type decompositions with strong orthogonal prediction innovations exist in smooth, reflexive Banach spaces of discrete time processes if and only if the projection operator generating the innovations satisfies the property of iterations. His theory includes as special cases all previous Wold‐type decompositions of discrete time processes, completely characterizes when non‐linear heavy‐tailed processes obtain a strong‐orthogonal moving average representation, and easily promotes a theory of non‐linear impulse response functions for infinite‐variance processes. The author exemplifies his theory by developing a non‐linear impulse response function for smooth transition threshold processes, and discusses how to test decomposition innovations for strong orthogonality and whether the proposed model represents the best predictor. A data set on currency exchange rates allows him to illustrate his methodology.     

基于线性规划模型的政府绩效评价与资源优化配置研究

政府绩效管理是利用绩效信息协助设定同意的绩效目标,进行资源配置与优先顺序的安排,以告知管理者维持或改变既定目标计划的管理过程。线性规划方法从政府资源配置绩效分析的角度为政府绩效科学评价与政府资源优化配置提供了分析方法和工具。线性规划的偶线性规划的经济意义给出了政府各种资源最优配置的经济估价,结合线性规划的“松紧定理”可进一步对政府机构改革与政府管理资源进行分析,从而达到提高政府管理绩效的目标。     

INCOMPLETE DATA IN GENERALIZED LINEAR MODELS WITH CONTINUOUS COVARIATES

This paper proposes a method for estimating the parameters in a generalized linear model with missing covariates. The missing covariates are assumed to come from a continuous distribution, and are assumed to be missing at random. In particular, Gaussian quadrature methods are used on the E-step of the EM algorithm, leading to an approximate EM algorithm. The parameters are then estimated using the weighted EM procedure given in Ibrahim (1990). This approximate EM procedure leads to approximate maximum likelihood estimates, whose standard errors and asymptotic properties are given. The proposed procedure is illustrated on a data set.