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．     

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.     

信度函数在政府形象评价模型中的应用

在分析政府形象效能评价系统和信息置信度的基础上,建立了基于信度函数的政府形象评价模型。运用该函数建模分析了政府形象评估中的理念识别系统、行为识别系统、视觉识别系统、环境识别系统以及个人识别系统等五大不确定性评价问题。经过理论分析,该函数对政府形象的评估有较大的参考价值。此外,该函数也可以用于对其他类似复杂性系统的效能评估,具有普遍适用性。     

HEAVY-TAILED-DISTRIBUTED THRESHOLD STOCHASTIC VOLATILITY MODELS IN FINANCIAL TIME SERIES

To capture mean and variance asymmetries and time‐varying volatility in financial time series, we generalize the threshold stochastic volatility (THSV) model and incorporate a heavy‐tailed error distribution. Unlike existing stochastic volatility models, this model simultaneously accounts for uncertainty in the unobserved threshold value and in the time‐delay parameter. Self‐exciting and exogenous threshold variables are considered to investigate the impact of a number of market news variables on volatility changes. Adopting a Bayesian approach, we use Markov chain Monte Carlo methods to estimate all unknown parameters and latent variables. A simulation experiment demonstrates good estimation performance for reasonable sample sizes. In a study of two international financial market indices, we consider two variants of the generalized THSV model, with US market news as the threshold variable. Finally, we compare models using Bayesian forecasting in a value‐at‐risk (VaR) study. The results show that our proposed model can generate more accurate VaR forecasts than can standard models.     

Detection and correction of artificial shifts in climate series

Summary.  Many long instrumental climate records are available and might provide useful information in climate research. These series are usually affected by artificial shifts, due to changes in the conditions of measurement and various kinds of spurious data. A comparison with surrounding weather-stations by means of a suitable two-factor model allows us to check the reliability of the series. An adapted penalized log-likelihood procedure is used to detect an unknown number of breaks and outliers. An example concerning temperature series from France confirms that a systematic comparison of the series together is valuable and allows us to correct the data even when no reliable series can be taken as a reference.     

优化专业结构,提高毕业生就业竞争力

随着社会以及高等教育改革的不断深化,高校毕业生就业形势日益严峻,根据用人单位对大学毕业生综合素质的要求和高校毕业学生对学校教学的意见反馈以及就业工作的现状,高等学校应优化专业结构,调整教学计划和课程设置体系,改革人才培养模式,努力提高毕业生就业竞争力。     

大学英语多项选择考试模式面临挑战

大比例多项选择考试模式在大学英语四、六级考试中己实施多年 ,它对推动我国的外语教育功不可没。但在我国的国情发生了巨大变化、整个国家正朝着国际化社会快速发展、社会各界急需实用型外语人才的今天 ,多年不变的大比例多项选择考试模式己经显示出它的不足。特别是IELTS;ESOL等国际实用性测试模式不断引入我国 ,它的确面临着改进和完善的挑战。     

综合文科教育专业人才培养和课程体系的理论研究与教学实践

为适应基础教育改革,开办了综合文科教育专业,在思想政治教育、历史教育和地理教育三个专业进行“主辅修制”改革的基础上,探讨以素质教育为核心,由传统分科教育为主向现代综合教育转变,由专业学科知识为中心向知识整合为中心转变。兼顾初中综合改革发展需要和传统分科教学的现实,培养的学生既能胜任初中综合课程《公民》、《社会》或《人文教育》等的教学,又能适应政治、历史和思想品德等分科课程的教学,还能适应乡镇经济、文化和社会发展需要的新型复合型师资。本文坚持理论研究与实践探索相结合的原则,确定了符合教育规律和实际的综合文科教育专业全新的培养目标和规格,建立了人才培养新模式,构建了全新的教育内容和课程体系。     

基于知识链的管理

论述了知识链的有关概念和特点,通过分析以往的知识链模型,提出了改进的知识链模型;对知识链管理的机制进行了分析;从控制论的角度,对知识链内部知识成长的机制进行了分析,并指出知识链内部知识转移的影响因素,为知识管理的发展提供了一定的理论依据。