对单位根检验PT统计量响应面函数的研究

Elliott等人于1996年提出了针对特定备择假设具有最优特性的PT单位根检验统计量。这一检验方法引起了众多计量经济学家的重视,并被收入一些新版本的计量软件包。但在实际运用时由于现有的临界值是在原文献给出的4个样本容量相应临界值的基础上插值而得,因而准确性较低,使得检验结果的可靠性受到了影响。本文利用Monte Carlo模拟的方法给出了在相当广泛的样本容量范围内临界值的近似计算公式,并对响应面函数法与插值法的结果做了一个简单的比较。     

基于不恒定样本容量的产品质量控制方法及实例分析

本文探讨了不恒定样本容量的产品质量控制方法。并根据实际数据,用这种方法分析了铅笔板从白板到成品的加工过程,即“软化板”过程。     

单位根检验中的Wald统计量研究

本文研究了不同情形下单位根的Wald检验.首先从理论上推导了各种Wald检验统计量的极限分布;然后,运用蒙特卡罗试验,模拟了有限样本容量常用检验水平下的临界值,拟合了临界值关于样本容量的响应面函数,给出了检验统计量的分位数表,并总结了用于单位根检验的Wald统计量有限样本容量下的统计特性.     

贝叶斯方法在指数编制中的应用

本文首先阐述了统计指数的实质,并以此为基础介绍了贝叶斯方法及其与统计指数的关系,最后结合上证180指数与居民消费价格指数,对统计指数的贝叶斯方法作了实证分析。     

贝叶斯非线性分层模型在多元索赔准备金评估中的应用

本文将贝叶斯非线性分层模型应用于基于不同业务线的多元索赔准备金评估中,设计了一种合适的模型结构,将非线性分层模型与贝叶斯方法结合起来,应用WinBUGS软件对精算实务中经典流量三角形数据进行建模分析,并使用MCMC方法得到了索赔准备金完整的预测分布。这种方法扩展并超越了已有多元评估方法中最佳估计和预测均方误差估计的研究范畴。在贝叶斯框架下结合后验分布实施推断对非寿险公司偿付能力监管和行业决策具有重要作用。     

基于改进的朴素贝叶斯恶意代码检测技术研究

文章介绍了传统的朴素贝叶斯恶意代码检测算法,并在传统朴素贝叶斯算法基础上提出改进,引入贡献系数以增大特征项对合法代码与恶意代码区分的能力,实验表明该方法有效提高了恶意代码的检测率和精准率,并且降低了恶意代码的误报率。     

基于贝叶斯网络方法的客户忠诚研究

提高客户忠诚是企业的核心原则。在与传统的数据挖掘及决策分析方法比较的基础上,提出了用贝叶斯网络方法来对客户忠诚进行研究。用贝叶斯网络方法对客户数据进行分析,确定影响客户忠诚的各因素之间的贝叶斯网络结构,得出这些因素之间的相互决定关系,为采取有效措施提高客户忠诚提供了理论依据。     

基于贝叶斯方法的开放式基金业绩评价

已有的基金业绩评价往往忽视直觉与数据分析相结合.为此,引入贝叶斯方法,将可能获得的先验信息与样本数据相结合,对传统的业绩评价方法进行改进.提出从投资者角度评价基金业绩的贝叶斯评价方法.     

贝叶斯决策分析方法在工程投标决策中的应用

文章利用贝叶斯决策分析方法对工程项目进行计算,确定最优报价。针对工程项目广泛采用招标投标方式的要求,利用贝叶斯决策分析方法对承包商依据招标文件的要求,就如何进行合理投标决策进行分析。贝叶斯决策分析方法在工程投标决策中可行性高,能够使承包商在一定中标概率下确定合理报价。     

基于贝叶斯方法的项目投资风险决策

由于投资受政策、社会环境、经济环境、管理水平等诸多因素的制约，因而投资成败的不确定性极大。本文将贝叶斯决策理论应用于投资决策中，建立了投资贝叶斯风险决策模型，分析了决策模型中各种参数的确定方法，并阐述了该模型对降低决策风险的作用。在风险决策中，信息的价值可以定量，运用贝叶斯公式分析在风险决策中增大信息量，有益于降低决策风险。     

Discriminating between autoregressive forms: A Monte Carlo comparison of Bayesian and ad hoc methods

The paper considers the problem of discriminating between the autoregressive forms of a Koyck distributed lag model and a regression model with autocorrelated distrubances. Several interpretations of an ad hoc rule-of-thumb suggested by Griliches are compared with Bayesian posterior odds analysis in a Monte Carlo experiment. The Bayesian analysis is generally superior to the rules-of-thumb, the latter exhibiting large probabilities of type I error, and low power. The rules-of-thumb excessively favour the distributed lag model, while the Bayesian method is free from such bias. All methods improve with increased sample size.     

Forecasting from others’ experience: Bayesian estimation of the generalized Bass model

We propose a Bayesian estimation procedure for the generalized Bass model that is used in product diffusion models. Our method forecasts product sales early based on previous similar markets; that is, we obtain pre-launch forecasts by analogy. We compare our forecasting proposal to traditional estimation approaches, and alternative new product diffusion specifications. We perform several simulation exercises, and use our method to forecast the sales of room air conditioners, BlackBerry handheld devices, and compressed natural gas. The results show that our Bayesian proposal provides better predictive performances than competing alternatives when little or no historical data are available, which is when sales projections are the most useful.     

UNCERTAINTY AND DENSITY FORECASTS OF ARMA MODELS: COMPARISON OF ASYMPTOTIC,BAYESIAN, AND BOOTSTRAP PROCEDURES

The objective of this paper is to analyze the effects of uncertainty on density forecasts of stationary linear univariate ARMA models. We consider three specific sources of uncertainty: parameter estimation, error distribution, and lag order. Depending on the estimation sample size and the forecast horizon, each of these sources may have different effects. We consider asymptotic, Bayesian, and bootstrap procedures proposed to deal with uncertainty and compare their finite sample properties. The results are illustrated constructing fan charts for UK inflation.     

The effect of estimation method and sample size in multilevel structural equation modeling

Multilevel structural equation modeling (multilevel SEM) has become an established method to analyze multilevel multivariate data. The first useful estimation method was the pseudobalanced method. This method is approximate because it assumes that all groups have the same size, and ignores unbalance when it exists. In addition, full information maximum likelihood (ML) estimation is now available, which is often combined with robust chi‐squares and standard errors to accommodate unmodeled heterogeneity (MLR). In addition, diagonally weighted least squares (DWLS) methods have become available as estimation methods. This article compares the pseudobalanced estimation method, ML(R), and two DWLS methods by simulating a multilevel factor model with unbalanced data. The simulations included different sample sizes at the individual and group levels and different intraclass correlation (ICC). The within‐group part of the model posed no problems. In the between part of the model, the different ICC sizes had no effect. There is a clear interaction effect between number of groups and estimation method. ML reaches unbiasedness fastest, then the two DWLS methods, then MLR, and then the pseudobalanced method (which needs more than 200 groups). We conclude that both ML(R) and DWLS are genuine improvements on the pseudobalanced approximation. With small sample sizes, the robust methods are not recommended.     

贝叶斯估计的图像分割技术

针对图像在动态阈值选取难的问题,通过比较全局阈值和局部阈值优缺点,选用贝叶斯阈值估计和迭代加权的方法对图像进行二值化分割,建立基于贝叶斯线性回归模型对检测到的阈值进行分析,通过图像增强,建立目标与非目标区域,分别计算各个区域的先验概率,使用贝叶斯估计模型求得似然函数的极小值即为后验概率,通过此模型对125组阈值样本进行分类,对异常阈值的判断率为14.4%,选取后的阈值更为精确。本文方法,既能有效的提取目标特征,较好的去除背景,又能够保留目标图像的细节。     

The Rule of Three, its Variants and Extensions

The Rule of Three (R3) states that 3/ n  is an approximate 95% upper limit for the binomial parameter, when there are no events in  n  trials. This rule is based on the one-sided Clopper–Pearson exact limit, but it is shown that none of the other popular frequentist methods lead to it. It can be seen as a special case of a Bayesian R3, but it is shown that among common choices for a non-informative prior, only the Bayes–Laplace and Zellner priors conform with it. R3 has also incorrectly been extended to 3 being a "reasonable" upper limit for the number of events in a future experiment of the same (large) size, when, instead, it applies to the binomial mean. In Bayesian estimation, such a limit should follow from the posterior predictive distribution. This method seems to give more natural results than—though when based on the Bayes–Laplace prior technically converges with—the method of prediction limits, which indicates between 87.5% and 93.75% confidence for this extended R3. These results shed light on R3 in general, suggest an extended Rule of Four for a number of events, provide a unique comparison of Bayesian and frequentist limits, and support the choice of the Bayes–Laplace prior among non-informative contenders.     

我国经济周期波动态势的区域划分与动态特征检验

本文利用贝叶斯估计结合门限自回归模型,对我国经济周期波动态势进行了分析和判断,检验中发现我国经济周期波动动态机制变迁的门限增长率为9.36%.当经济增长率高于这个门限值后,经济周期波动具有自稳定的均值回归特征;当经济增长率低于这个门限值后,经济周期波动具有向更高稳态的迁移特征,这意味着我国现阶段的经济增长过程具有内生的稳定性,这也是近年来我国经济波动率趋于稳定的重要原因.从经济周期波动的动态轨迹判断,我国经济仍然具备保持快速稳定增长的基础,因此需要利用有效宏观调控来保证快速稳定增长的实现.     

Bayesian modeling of joint and conditional distributions

In this paper, we study a Bayesian approach to flexible modeling of conditional distributions. The approach uses a flexible model for the joint distribution of the dependent and independent variables and then extracts the conditional distributions of interest from the estimated joint distribution. We use a finite mixture of multivariate normals (FMMN) to estimate the joint distribution. The conditional distributions can then be assessed analytically or through simulations. The discrete variables are handled through the use of latent variables. The estimation procedure employs an MCMC algorithm. We provide a characterization of the Kullback–Leibler closure of FMMN and show that the joint and conditional predictive densities implied by the FMMN model are consistent estimators for a large class of data generating processes with continuous and discrete observables. The method can be used as a robust regression model with discrete and continuous dependent and independent variables and as a Bayesian alternative to semi- and non-parametric models such as quantile and kernel regression. In experiments, the method compares favorably with classical nonparametric and alternative Bayesian methods.     

浅谈似然函数的两种不同定义

本文通过两个具体例题表明当连续型总体可能的取值范围不是(-∞,+∞)时,利用第一种似然函数的定义解决点估计问题,学生不仅能够很容易地掌握最大似然估计法,同时对样本来自总体且估计离不开样本这一统计思想加深了理解。     

Comparisons of Bayesian estimation procedures for two-way contingency tables without interaction

Bayesian and empirical Bayesian estimation methods are reviewed and proposed for the row and column parameters in two-way Contingency tables without interaction. Rasch's multiplicative Poisson model for misreadings is discussed in an example. The case is treated where assumptions of exchangeability are reasonable a priori for the unknown parameters. Two different types of prior distributions are compared, It appears that gamma priors yield more tractable results than lognormal priors.