教育心理学研究方法体系的发展历程与新取向

教育心理学在其独立成为一门学科时,就借鉴自然学科的研究方法初步建立起了包括研究方法论、研究方式、具体方法和技术三层次的研究方法体系。经过学科建立20世纪到50年代的发展和60年代后的完善,逐渐形成了较为完善的教育心理学研究方法体系。在新的历史条件下,其研究方法论、方式和方法表现出综合化、生态化、科学化、本土化等发展取向。     

对“资本限量决策”方法的改进

企业面临的一类投资决策是所谓的资本限量决策,文献1给出两种解法:获利指数法和净现值法。究其实质,净现值法是一种枚举法或互斥化法,该方法可以获得最优解,但当项目个数较多时,互斥化的组合方案数很大,难以实用;计算加权获利指数工作量又很大。本文提出用差量效率指标排序法予以改进。     

EXCEL的广泛应用

EXCEL电子表格处理数据的功能强大,应用简单且广泛,本文用两个例子具体阐述了EXCEL两个方面的功能。     

国外生态税收实践及对中国的启示

国外通过开征二氧化碳税、二氧化硫税、资源开发税、噪音税、拥挤税以及环境关税等加强生态环境保护,但中国目前尚未建立以保护生态环境为目的生态税收体系,存在着主体税种缺位、相关税种环保因素考虑不够、生态税收优惠形式单一等问题。建议根据可持续发展理论,借鉴国外经验,通过开征专门的环境保护税、改革相关税种、完善税收优惠等措施,构建以生态税收为主要内容的税制新体系。     

斜腿刚构桥几何布局的优化设计

采用数学规划的方法从静力和动力两方面对斜腿刚构桥的几何布局进行优化设计。静力优化设计的优化目标是截面截面应力平方均值最小，动力优化设计的优化目标是结构自振周期平方和最小。采用了直接搜索法寻优。通过算例可知，这两种优化设计方法均可行，且均为刚性设计。     

MODEL-ASSISTED HIGHER-ORDER CALIBRATION OF ESTIMATORS OF VARIANCE

In survey sampling, interest often centres on inference for the population total using information about an auxiliary variable. The variance of the estimator used plays a key role in such inference. This study develops a new set of higher‐order constraints for the calibration of estimators of variance for various estimators of the population total. The proposed strategy requires an appropriate model for describing the relationship between the response and auxiliary variable, and the variance of the auxiliary variable. It is therefore referred to as a model‐assisted approach. Several new estimators of variance, including the higher‐order calibration estimators of the variance of the ratio and regression estimators suggested by Singh, Horn & Yu and Sitter & Wu are special cases of the proposed technique. The paper presents and discusses the results of an empirical study to compare the performance of the proposed estimators and existing counterparts.     

关于物流的科学分类问题（续）

本文最新提出物流最基本的四个固有属性：物（Material）的根本属性，流（Flow）的浪本属性，物和流的主体（Party）属性，物和流的地域（Region）属性。无论自然界物流，还是社会界物流，经济界物流，都具有这四个方面属性（RPMF）。同时，本文首次提出物流四要素RPMF原理。根据物流四要素RPMF原理，提出按各要素的不同属性对物流进行科学分类的方法，并给出物流分类图。     

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.     

教育世界的特性

教育世界是教育科学研究的对象域。教育世界的特性是本质世界。教育世界的本质世界特性是以人的本质力量的外在表现物——哲学和各门学科知识体系为手段培养人性——人的本质。教育世界的本质特点就是本质教育。人的本质表现在责任、方法和美。教育世界的本质世界特性决定了教育科学属于本质科学。     

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.