基于离散选择模型DCM的产品差异市场分析

本文从计量经济学的角度,利用离散选择模型对产品差异市场进行了研究,在消费者异质性需求和厂商异质性能力假设下,利用消费者产品性能边际效用递减规律推导出产品性能消费者收益函数,利用产品性能边际成本递增规律推导出产品性能厂商成本关系函数,最后得到消费者需求弹性和厂商在最大创造价值的产品性能决策点.     

高维模型选择方法综述

模型选择是统计学的热点研究问题。近年来随着数据维数越来越高,传统模型选择方法的应用受到了很多制约。本文着重介绍高维模型选择的新方法,并讨论实现模型选择过程的一个重要环节,即调整参数的选取。最后文章总结归纳了未来可能的研究方向。     

GARCH模型估计方法选择及对上证指数的应用

本文介绍了对ARCH/GARCH模型的两种估计方法:准极大似然估计和极小绝对偏差估计,并提出了一种基于自助法(Bootstrap)对估计方法的选择。在厚尾程度不同的情况下进行了模拟分析,表明对于一个具体的数据,该选择法能够自动选择较优的估计方法。并用该方法对上海证券交易所A股和B股的股价指数进行了分析,印证了上海股市B股收益率的尾部厚于A股收益率尾部。     

一类新的非线性离散Bihari型不等式解的估计

本文建立了一类新的具有m项非线性离散Bihaxi型不等式解的估计,所得结果推广了过去关于非线性离散Bihari型不等式的相关结果,并用实例给出了解的估计.     

一般相依索赔离散风险模型的破产概率渐近估计及数值模拟

研究一类具有利率和相依索赔额的离散风险模型.在模型中,索赔额服从具有独立同分布步长的单边线性过程,贴现因子具有关于利率与时间的一般函数形式.在步长服从重尾分布的条件下,得到了最终破产概率的渐近估计.并通过具体实例分析利率对破产概率的影响.     

机场选择研究综述

随着机场群都市区的形成,分析旅客对登机机场的选择就成为航空运输规划一个重要环节.近年来,对它的研究已经成为西方发达国家航空运输领域的前沿热点问题之一。鉴于我国在这方面的研究还未展开,因此有必要对国外已有的研究进行系统阐述。首先阐明了机场群都市区形成所引发的机场选择问题,接着回顾了国外对这一问题的研究历史,归纳已取得的重要研究成果和基础数据描述,并对"参数化随机选择集多项Logit"模型这一新进展作了重点介绍,最后提出一些需要深入研究的建议。     

灰色动态模型及其在人口预测中的应用

一个国家人口的数量直接影响着其经济、社会的发展和资源的利用 ,中国是世界上人口的第一大国 ,人口问题一直是制约中国发展的第一因素 .本文应用灰色系统等维灰数递补动态预测模型 ,对中国未来5 0年的人口数量进行了动态预测 ,通过检验表明 ,该模型合理、方法简便可行、结果符合实际 ,为中国经济和社会发展的决策和研究提供了科学依据     

离散的Burgers-Ginzburg-Landau方程组的吸引子

文章通过对空间变量的有限差分方法离散了具有周期边值的Ｂｕｒｇｅｒｓ Ｇｉｎｚｂｕｒｇ Ｌａｎｄａｕ方程组．研究了这个离散方程组初值问题解的适定性．证明了当差分网格足够大时离散方程组存在吸引子，并得到了吸引子的Ｈａｕｓｄｏｒｆｆ维数和分形维数的上界估计．这个上界不会随着网格的加细而无限增大，因此数值分析离散的有限维系统的吸引子可以近似探讨原无限维系统的吸引子．     

Mixed Estimations and Bayes Estimation of theRegression Coefficient in Multivariate Linear Model

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A note on the expressive power of deep rectified linear unit networks in high‐dimensional spaces

We investigate the ability of deep deep rectified linear unit (ReLU) networks to approximate multivariate functions. Specially, we establish the approximation error estimate on a class of bandlimited functions; in this case, ReLU networks can overcome the “curse of dimensionality.”     

非时齐扩散模型中时变参数的局部线性估计

本文主要研究非时齐扩散模型中时变的漂移参数和扩散参数的局部线性估计。基于非时齐扩散模型的离散观测样本,首先得到了漂移参数的局部线性估计及其标准误差。然后,考虑到扩散参数的非负性,本文利用局部对数线性拟合的方法得到了扩散参数的核函数加权估计,并讨论了扩散项估计的渐近偏差、渐近方差和渐近正态性。最后,通过模拟研究表明所得局部估计有很好的拟合效果。     

Reviews on the Research of Dynamic Mortality Models

Mortality forecasting is the basis of population forecasting. In recent years, new progress has been made in mortality models. From the earliest static mortality models, mortality models have been developed into dynamic forecasting models including time terms, such as Lee-Carter model family, CBD model family and so on. This paper reviews and sorts out relevant literature on mortality forecasting models. With the development of dynamic models, some scholars have developed a series of mortality improvement models based on the level of mortality improvement. In addition, with the progress of mortality research, multi-population mortality modeling attracted the attention of researchers, and the multi-population forecasting models have been constantly developed and improved, which play an important role in the mortality forecasting. With the continuous enrichment and innovation of mortality model research methods, new statistical methods (such as machine learning) have been applied in mortality modeling, and the accuracy of fitting and prediction has been improved. In addition to the extension of classical modeling methods, issues such as small-area population or missing data of the population, the elderly population, the related population mortality modeling are still worth studying.     

Approximating Networks and Extended Ritz Method for the Solution of Functional Optimization Problems

Functional optimization problems can be solved analytically only if special assumptions are verified; otherwise, approximations are needed. The approximate method that we propose is based on two steps. First, the decision functions are constrained to take on the structure of linear combinations of basis functions containing free parameters to be optimized (hence, this step can be considered as an extension to the Ritz method, for which fixed basis functions are used). Then, the functional optimization problem can be approximated by nonlinear programming problems. Linear combinations of basis functions are called approximating networks when they benefit from suitable density properties. We term such networks nonlinear (linear) approximating networks if their basis functions contain (do not contain) free parameters. For certain classes of d-variable functions to be approximated, nonlinear approximating networks may require a number of parameters increasing moderately with d, whereas linear approximating networks may be ruled out by the curse of dimensionality. Since the cost functions of the resulting nonlinear programming problems include complex averaging operations, we minimize such functions by stochastic approximation algorithms. As important special cases, we consider stochastic optimal control and estimation problems. Numerical examples show the effectiveness of the method in solving optimization problems stated in high-dimensional settings, involving for instance several tens of state variables.     

连续性抽样调查理论研究综述

从国外近60多年来的理论及其应用研究情况来看,连续性抽样调查是一个具有极大理论研究价值的新领域,在我国也具有广阔的应用价值。本文选择连续性抽样调查作为研究对象,对国内外已有的相关研究成果进行理论化、系统化的研究综述,并重点总结了各类连续性抽样设计与抽样估计方法,进一步归纳出存在的问题及未来继续研究的新趋势,另外也为该理论在我国实际调查中的应用研究奠定扎实的理论基础,使我国统计调查工作少走弯路,尽快与国外统计调查工作接轨。     

基于Bootstrap法的动态修正贝叶斯精度评估

针对工程实际中所普遍存在的小子样条件下,研究具有多阶段试验信息时的参数估计问题,提出一种结合Bootstrap方法与动态修正贝叶斯估计方法对精度评估中的弹点散布方差进行估计的方案.     

Convex Models of High Dimensional Discrete Data

Categorical data of high (but finite) dimensionality generate sparsely populated J-way contingency tables because of finite sample sizes. A model representing such data by a "smooth" low dimensional parametric structure using a "natural" metric would be useful. We discuss a model using a metric determined by convex sets to represent moments of a discrete distribution to order J. The model is shown, from theorems on convex polytopes, to depend only on the linear space spanned by the convex set—it is otherwise measure invariant. We provide an empirical example to illustrate the maximum likelihood estimation of parameters of a particular statistical application (Grade of Membership analysis) of such a model.