BAYESIAN SUBSET SELECTION AND MODEL AVERAGING USING A CENTRED AND DISPERSED PRIOR FOR THE ERROR VARIANCE

This article proposes a new data‐based prior distribution for the error variance in a Gaussian linear regression model, when the model is used for Bayesian variable selection and model averaging. For a given subset of variables in the model, this prior has a mode that is an unbiased estimator of the error variance but is suitably dispersed to make it uninformative relative to the marginal likelihood. The advantage of this empirical Bayes prior for the error variance is that it is centred and dispersed sensibly and avoids the arbitrary specification of hyperparameters. The performance of the new prior is compared to that of a prior proposed previously in the literature using several simulated examples and two loss functions. For each example our paper also reports results for the model that orthogonalizes the predictor variables before performing subset selection. A real example is also investigated. The empirical results suggest that for both the simulated and real data, the performance of the estimators based on the prior proposed in our article compares favourably with that of a prior used previously in the literature.     

城市规划专业人才培养方案探讨

根据我校建筑工程管理系及国内城市规划专业教育的现状,对城市规划专业课程体系进行了适当调整,提出应确立具有自己特色的、服务地方的应用型人才的培养模式。     

深圳证券交易所不同指数收益率的波动比较

股票价格指数的波动的大小通常代表了它的风险程度,研究股指的波动对风险管理、投资组合以及价格预测有着十分重要的意义。文章采用时间序列分析方法,对深圳证券交易所的三种价格指数建立GARCH模型,并对这三种指数的波动率进行比较。     

高等院校英语强化课程的评估体系与评估模式的构建

文章选取培正商学院的一年英语强化教改项目作为研究个案,根据(背景—输入—过程—输出CIPP)评估理论,利用质性评价和量性评价相结合的手段,对强化项目的背景和过程进行了评价。并由此构建出了一个针对高职高专院校公共外语教学的、具有自我约束并持续改进的课程评估体系。     

翻译理论新标准与语言学理论

语言学的发展一直在不断地影响着翻译理论的发展,只不过有时这种影响很间接,尼达的译学思想在中国翻译界一向占据着重要的地位,但这位大师最近宣布其观点已经发生根本变化,尼达学术观点的变化无疑会给目前译学建设带来一定的负面影响。本文着重从翻译理论的作用、翻译标准及语言学与翻译的关系三个角度对尼达翻译思想的转变加以分析;重新阐述理论在译学框架中的地位及作用,理论和实践的关系及翻译理论的意义。     

电力市场理论的若干基本概念辨析

本文通过对电力市场、电价与电力交易模式等电力经济基础概念的分析,指出了现有概念体系的不足,并提出了作者的认识,尤其是对电力市场一般与市场特殊作出详尽分析,力图为构建竞争性的电力市场经济理论奠定微观概念的基础。     

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.     

造粒塔内液滴传热数学模型的建立

通过在竖直向上流动的空气中逆向运动的颗粒的速率方程．单颗粒与流动气流间的Ranz和Marshall传热关联式．以及能量守恒方程和传热速率方程建立简化的液滴粒径与塔高间的定量关系．从而为研究喷雾造粒工艺提供参考．     

基尼系数合意值和警戒线的探讨

 近年来，很多学者对基尼系数的经验法则尤其是警戒线提出了质疑。本文首先分析了收入分配的合意类型，然后依据相对贫困、贫富差距及中等收入者的收入份额三个指标来界定合意的收入分配，最后根据合意收入分配确定基尼系数的合意值和警戒线。本文结论表明，0.3—0.4可以被认为是基尼系数的合意值,0.4作为基尼系数警戒线也是比较合理的。     

Bayesian analysis of a marked point process: Application in seismic hazard assessment

Point processes are the stochastic models most suitable for describing physical phenomena that appear at irregularly spaced times, such as the earthquakes. These processes are uniquely characterized by their conditional intensity, that is, by the probability that an event will occur in the infinitesimal interval (t, t+Δt), given the history of the process up tot. The seismic phenomenon displays different behaviours on different time and size scales; in particular, the occurrence of destructive shocks over some centuries in a seismogenic region may be explained by the elastic rebound theory. This theory has inspired the so-called stress release models: their conditional intensity translates the idea that an earthquake produces a sudden decrease in the amount of strain accumulated gradually over time along a fault, and the subsequent event occurs when the stress exceeds the strength of the medium. This study has a double objective: the formulation of these models in the Bayesian framework, and the assignment to each event of a mark, that is its magnitude, modelled through a distribution that depends at timet on the stress level accumulated up to that instant. The resulting parameter space is constrained and dependent on the data, complicating Bayesian computation and analysis. We have resorted to Monte Carlo methods to solve these problems.