BURR CRITICAL VALUE APPROXIMATIONS FOR TESTS OF AUTOCORRELATION AND HETEROSCEDASTICITY

The paper evaluates the accuracy of Burr approximations of critical values and p-values for test a of autocorrelation and heteroscedasticity in the linear regression model.     

线性矩阵不等式问题的进化计算解决方法

针对决定模糊控制中稳定性的线性矩阵不等式问题,提出了用进化计算来解决模糊控制中线性矩阵不等式的新算法。实验证明,该算法解“用于实现模糊控制的增益调度和稳定性的线性矩阵不等式”是有效的。     

Generalized additive models for location, scale and shape

Summary.  A general class of statistical models for a univariate response variable is presented which we call the generalized additive model for location, scale and shape (GAMLSS). The model assumes independent observations of the response variable y given the parameters, the explanatory variables and the values of the random effects. The distribution for the response variable in the GAMLSS can be selected from a very general family of distributions including highly skew or kurtotic continuous and discrete distributions. The systematic part of the model is expanded to allow modelling not only of the mean (or location) but also of the other parameters of the distribution of y , as parametric and/or additive nonparametric (smooth) functions of explanatory variables and/or random-effects terms. Maximum (penalized) likelihood estimation is used to fit the (non)parametric models. A Newton–Raphson or Fisher scoring algorithm is used to maximize the (penalized) likelihood. The additive terms in the model are fitted by using a backfitting algorithm. Censored data are easily incorporated into the framework. Five data sets from different fields of application are analysed to emphasize the generality of the GAMLSS class of models.     

Discrete time modelling of disease incidence time series by using Markov chain Monte Carlo methods

Summary.  A stochastic discrete time version of the susceptible–infected–recovered model for infectious diseases is developed. Disease is transmitted within and between communities when infected and susceptible individuals interact. Markov chain Monte Carlo methods are used to make inference about these unobserved populations and the unknown parameters of interest. The algorithm is designed specifically for modelling time series of reported measles cases although it can be adapted for other infectious diseases with permanent immunity. The application to observed measles incidence series motivates extensions to incorporate age structure as well as spatial epidemic coupling between communities.     

Bayesian updating of atmospheric dispersion after a nuclear accident

Summary.  We consider a Bayesian forecasting system to predict the dispersal of contamination on a large scale grid in the event of an accidental release of radioactivity. The statistical model is built on a physical model for atmospheric dispersion and transport called MATCH. Our spatiotemporal model is a dynamic linear model where the state parameters are the (essentially, deterministic) predictions of MATCH; the distributions of these are updated sequentially in the light of monitoring data. One of the distinguishing features of the model is that the number of these parameters is very large (typically several hundreds of thousands) and we discuss practical issues arising in its implementation as a realtime model. Our procedures have been checked against a variational approach which is used widely in the atmospheric sciences. The results of the model are applied to test data from a tracer experiment.     

Temporal pattern in number of staff on sick leave: the effect of downsizing

Summary.  The pattern of absenteeism in the downsizing process of companies is a topic in focus in economics and social science. A general question is whether employees who are frequently absent are more likely to be selected to be laid off or in contrast whether employees to be dismissed are more likely to be absent for the remaining time of their working contract. We pursue an empirical and microeconomic investigation of these theses. We analyse longitudinal data that were collected in a German company over several years. We fit a semiparametric transition model based on a mixture Poisson distribution for the days of absenteeism per month. Prediction intervals are considered and the primary focus is on the period of downsizing. The data reveal clear evidence for the hypothesis that employees who are to be laid off are more frequently absent before leaving the company. Interestingly, though, no clear evidence is seen that employees being selected to leave the company are those with a bad absenteeism profile.     

大中专在校学生考试分数的正态化线性转换

本文分析了考试记分方法中传统的绝对百分体制的缺陷，给出了一种正态化转换分数的算法，较适合于在校大中专学生学习水平的横向评价。     

一般Gauss－Markov模型下一般可估函数的线性充分性和线性完全性

发展了Ｂａｋｓａｌａｒｙ和Ｄｒｙｇａｓ提出的一般Ｇａｕｓｓ－Ｍａｒｋｏｖ模型中线性充分性、最小充分性和完全性的概念，用Ｒａｏ的最小二乘统一理论，给出了这些概念的刻划定理。     

Residual analysis for spatial point processes (with discussion)

Summary.  We define residuals for point process models fitted to spatial point pattern data, and we propose diagnostic plots based on them. The residuals apply to any point process model that has a conditional intensity; the model may exhibit spatial heterogeneity, interpoint interaction and dependence on spatial covariates. Some existing ad hoc methods for model checking (quadrat counts, scan statistic, kernel smoothed intensity and Berman's diagnostic) are recovered as special cases. Diagnostic tools are developed systematically, by using an analogy between our spatial residuals and the usual residuals for (non-spatial) generalized linear models. The conditional intensity λ plays the role of the mean response. This makes it possible to adapt existing knowledge about model validation for generalized linear models to the spatial point process context, giving recommendations for diagnostic plots. A plot of smoothed residuals against spatial location, or against a spatial covariate, is effective in diagnosing spatial trend or co-variate effects. Q – Q -plots of the residuals are effective in diagnosing interpoint interaction.     

一类多元Gauss-Weierstrass算子线性组合的加权逼近

该文给出了一类多元Gauss-Weierstrass算子线性组合加Jacobi权在一致逼近下的正、逆定理和逼近阶的特征刻划