相依删失情形下加速失效模型的广义M估计

在生存分析中,加速失效模型(AFT模型)是研究失效时问与协变量之间关系的一类重要模型.在AFT模型框架下,通过在广义M估计的目标函数中使用广义Kaplan-Meier权,本文提出了含相依删失数据的AFT模型回归系数及刻度参数的加权广义M估计.渐近性质方面,得到了广义M估计的相合性和渐近正态性;关于估计量的有限样本性质,通过模拟研究验证了该估计量在有限样本情形下效果良好.     

混合效应模型及其在非寿险费率厘定中的应用

在非寿险分类费率厘定中,广义线性模型的应用十分普遍,但当某些费率因子的水平数很多时(本文称之为多水平因子),广义线性模型的估计结果将不可靠。解决此类问题的一种方法是把多水平费率因子作为随机效应处理。将多水平费率因子作为随机效应处理可以采取下述三种方法:(1)分别用广义线性模型和信度模型估计普通费率因子和多水平因子,通过广义线性模型与Buhlmann-Straub信度模型的迭代应用预测索赔频率和索赔强度;(2)应用广义线性混合模型分别预测索赔频率和索赔强度;(3)直接对经验纯保费数据建立Tweedie混合效应模型。本文把上述模型应用于中国车损险实际数据的研究结果表明,这三种方法比较接近,但从总体上看,广义线性混合模型的估计结果更加可取。     

基于NS混合模型的中国利率期限结构动态估计比较研究

采用NS混合模型动态估计中国利率期限结构,考察动态NS模型,无套利NS模型及广义无套利NS模型等NS混合模型对我国利率期限结构的动态估计效率,比较NS混合模型的样本外预测能力,检验无套利约束对混合模型动态估计的影响.本文的经验分析结果表明:无套利条件的引入增强了NS混合模型的样本内动态估计能力和样本外预测能力;五因素的广义无套利NS模型(AFGNS)无论在利率期限结构样本内动态估计还是在总体预测效率上都要高于其他模型,可将其作为利率期限结构研究的基础模型:     

具有自相关误差结构的面板数据部分线性单指标模型的统计推断

部分线性单指标模型是在科学研究中具有广泛应用的经典半参数模型之一.本文主要研究具有自相关误差结构的面板数据的部分线性单指标模型的统计推断问题.通过结合局部多项式和纠偏广义估计方程方法,本文提出模型参数的可行加权广义估计(feasible weighted generalized estimating equation estimation, GEE-FW),证明该估计具有相合性和渐近正态性,并且在渐近方差意义下阐明该估计比工作独立的广义估计(generalized estimating equation estimation based on working independence,GEE-WI)更加有效.此外,本文对模型中未知连接函数提出两阶段局部线性估计(two step local linear generalized estimating equation estimation, GEE-TS),建立该估计的渐近性质.数值模拟研究和实际数据分析都表明了本文所提出的方法是有效的,在理论和应用方面均具有良好的表现.     

推广的生长曲线模型中未知参数矩阵的广义最小二乘估计的可容许性

本文在设计矩阵与结构矩阵分别正交的条件下,研究了推广的生长曲线模型未知参数矩阵的广义最小二乘估计.运用矩阵理论证明了此广义最小二乘估计在某个线性估计类中的可容许性.并对潘建新(1989)的结果的推广.     

基于联合估计方法的广义随机系数自回归模型的统计推断

本文利用联合估计函数方法(CEF)对广义随机系数自回归(GRCA)模型进行统计研究.应用联合估计函数方法得到广义随机系数自回归模型参数估计量,证明了提出的参数估计量的相合性和渐近正态性,利用数值模拟对提出的参数统计量进行对比分析,数值模拟结果表明,联合估计方法的参数估计量优于基于估计函数方法、伪极大似然方法、最小二乘方法的参数估计量,实证研究也说明CEF方法具有较好的效果.     

增长曲线模型中回归系数的广义根方估计

本文对增长曲线模型中的回归系数B提出了一种新的估计形式－广义根方估计的B（K），其中K＝diag(k1,k2,…，kp）并证明了通过广义根方偏参数ki(i=1,2,…，p）的适当选取可使得该估计在均方误差矩阵的意义下优于已有的LS估计和根方估计，及广义根方估计是可容许估计，本文还给出了选取广义根方偏参数的两种方法，算法及一个应用实例。     

多元线性混合模型方差分量矩阵的非负估计

本文研究了带有两个方差分量矩阵的多元线性混合模型方差分量矩阵的估计问题.对于平衡模型,给出了基于谱分解估计的一个方差分量矩阵的非负估计类.对于非平衡模型,给出了方差分量矩阵的广义谱分解估计类,讨论了与ANOVA估计等价的充要条件.同时,在广义谱分解估计的基础上给出了一种非负估计类,并讨论了其优良性.当具有较小二次风险的非负估计不存在时,从估计为非负的概率的角度考虑,将Kelly和Mathew(1993)提出的构造具有更小取负值概率的估计类的方法推广到本文的多元模型下,给出了较谱分解估计相比有更小取负值概率和更小风险的估计类.最后,模拟研究和实例分析表明文中理论结果有很好的表现.     

生长曲线模型中参数的Bayes线性无偏估计

本文在平方损失下导出了生长曲线模型中参数的Bayes线性无偏估计(LUE), 并在均方误差矩阵(MSEM)准则下研究了Bayes LUE相对于广义最小二乘估计(GLSE)的优良性. 对于非满秩情形,获得了可估函数的Bayes LUE并讨论了其优良性问题.     

Panel模型中常见估计的比较

本文研究了Panel模型中回归系数常见估计的比较问题,给出了在Pitman准则,协方差阵准则和广义均方误差准则下最小二乘估计,Within估计,Between估计及两步估计之间的优良性比较结果.特别地,本文证明了在Pitman准则下最小二乘估计一致地优于Between估计.     

混合双参数广义Pareto分布的参数估计

Pareto分布族因其厚尾特点,在金融分析、寿命分析中都是非常重要的统计模型.但是对于混合双参广义Pareto分布,在模型参数估计时,传统的矩法估计和极大似然估计在理论上可以实现,实践时比较困难.本文应用EM算法之ECM算法,研究了混合广义Pareto分布在完全数据场合下的参数估计问题,并模拟说明EM算法来估计混合广义Pareto分布是一种容易实现又非常有效的方法.     

混合指数分布的参数估计

混合指数分布是寿命数据分析中一个非常重要的统计模型\bd 但是利用正规的统计方法如矩估计、极大似然估计等估计模型的参数往往比较困难\bd 本文应用EM算法详细研究了混合指数分布在正常工作条件下和在进行恒加应力加速寿命实验条件下, 在完全数据场合、I-型截尾和II-型截尾场合的参数估计问题\bd 模拟说明利用EM算法来估计混合指数分布是一种非常有效的方法.     

Focus on economy or ecology? A three‐dimensional trade‐off based on ecological carrying capacity in southwest China

Economic growth and human activities challenge the regional harmony of the economy and environment. Based on an evaluation of ecological carrying capacity (ECC), we have constructed a three‐dimensional trade‐off model for socially harmonious development for the Sichuan and Yunnan regions in southwest China. The results demonstrate that there is a continuous increase in socioeconomic coordination (SEC; slope > 0.23) and a change in the ECC in the study area, characterized by subtle fluctuations from 2000 through 2008. This was primarily due to the loss of resource and environment carrying capacity wiping out the profits of SEC. Significant government aid during the postdisaster reconstruction and more attention being paid to the environment in policy resulted in a faster increase in ECC (slope > 0.38) from 2008 to 2016. Based on this trade‐off model, managers could deeply understand the relationship between resources, the economy, and ecology. Summary for Managers

• Ecological environment protection is increasingly crucial in Sichuan and Yunnan regions as they feel the effects of climate change and human activities.
• The three‐dimensional trade‐off model measuring socially coordinated development is a modified multiobjective, decision‐making model.
• Based on this trade‐off model, managers could, understand the relationship between resources, the economy, and ecology.
• It can help manage our environmental assets and plan ecological conservation, to ensure that the environment maintains its capacity to serve the societal development.

     

The influence of capital market laws and initial public offering (IPO) process on venture capital

This paper is concerned with the influence of capital market laws and initial public offering (IPO) process on venture capital. It discusses the impact of US federal state laws and Securities and Exchange Commission (SEC) regulations to the venture capital markets, arguing if the rules and regulatories are burdensome to entrepreneurs and new-growth businesses. The impact of Sarbanes-Oxley Act and the future Investment Act on venture capital funds and entrepreneurial companies going public are also discussed. The paper proposes the model of venture capital financing describing the process from fund raising to investment exits, the linkages of the venture capital market to the financial/capital markets and the related capital market laws. The policy implications on SEC regulations essential to the development of venture capital industry are suggested.     

Research on estimation model of the battery state of charge in a hybrid electric vehicle based on the classification and regression tree

ABSTRACT

In order to achieve the accurate estimation of state of charge (SOC) of the battery in a hybrid electric vehicle (HEV), this paper proposed a new estimation model based on the classification and regression tree (CART) which belongs to a kind of decision tree. The basic principle and modelling process of the CART decision tree were introduced in detail in this paper, and we used the voltage, current, and temperature of the battery in an HEV to estimate the value of SOC under the driving cycle. Meanwhile, we took the energy feedback of the HEV under the regenerative braking into consideration. The simulation data and experimental data were used to test the effectiveness of the estimation model of CART, and the results indicate that the proposed estimation model has high accuracy, the relative error of simulation is within 0.035, while the relative error of experiment is less than 0.05.     

动态离散选择模型及其估计方法发展综述

动态离散选择模型是一类离散控制动态规划,其对于理解机构在不同情况中的行为决策是非常重要的.从动态离散选择模型的基本原理、应用现状和构造性估计满足的假设出发,一方面针对动态选择模型的小样本异质性问题,给出非线性有偏修正模型和最小均方误差估计法来弥补最大似然估计在估计小样本时的有偏性.另一方面为了达到期望价值函数的收敛性和避免高维数所带来的计算负担,叙述了常规贝叶斯估计方法、贝叶斯-MCMC-DP估计法、马尔科夫链-蒙特卡洛-人工神经网络估计法等其他的估计方法.     

Data analysis for accelerated life tests via Weibull-gamma frailty regression models

In this article, we study data analysis methods for accelerated life test (ALT) with blocking. Unlike the previous assumption of normal distribution for random block effects, we advocate the use of Weibull regression model with gamma random effects for making statistical inference of ALT data. To estimate the unknown parameters in the proposed model, maximum likelihood estimation and Bayesian estimation methods are provided. We illustrate the proposed methods using real data examples and simulation examples. Numerical results suggest that distribution of random effects has minimal impact on the estimation of fixed effects in the Weibull regression models. Furthermore, to demonstrate the advantage of our proposed model, we also provide methods to compare ALT plans and thus identify the optimal ALT plans.     

基于Bayes估计的金融风险值——VaR计算

初步研究了用Bayes估计计算金融风险值VaR,同时阐明了运用极值理论方法在Bayes估计下的金融风险值计算。并且借助统计计算方法——MCMC算法来求解参数的Bayes估计,有效的将Bayes思想融入到了VaR的计算中。用Bayes估计计算金融风险值VsR,可以帮助投资者将观测数据和自己所掌握的经验信息对VaR模型进行调整,使得vsR模型能够更准确地反映出金融市场的风险状况,据此做出更加正确的投资决策。     

An Autoregressive Ordered Probit Model With Application to High-Frequency Financial Data

This article introduces a model that can be considered as an autoregressive extension of the ordered probit model. For parameter estimation we first develop a standard Gibbs sampler which however exhibits bad convergence properties. Using a special transformation group on the sample space we develop a grouped move multigrid Monte Carlo (GM-MGMC) Gibbs sampler and illustrate its fundamental superiority in convergence compared to the standard sampler. To be able to compare the autoregressive ordered probit (AOP) model to other models we further provide an estimation procedure for the marginal likelihood which enables us to compute Bayes factors. We apply the new model to absolute price changes of the IBM stock traded on December 4, 2000, at the New York Stock Exchange. To detect whether the data contain an autoregressive structure we then fit the AOP model as well as the common ordered probit (OP) model to the data. By estimating the corresponding Bayes factor we show that the AOP model fits the data decisively better than the common OP model.     

Reducing asymptotic bias of weak instrumental estimation using independently repeated cross-sectional information

In this paper, we consider the instrumental variable estimation (the two-stage least squares estimator and the limited information maximum likelihood estimator) using weak instruments in a repeated measurements or a panel data model. We show that independently repeated cross-sectional data can reduce the asymptotic bias of the instrumental variable estimation when instruments are weakly correlated with endogenous variables. When the number of repeated measurements tends to infinity, we can achieve consistent instrumental variable estimation with weak instruments.