一种证券组合选择模型

本文在Ｍａｒｋｏｗｉｔｚ组合证券投资决策模型基础上提出了一种可产生更优组合证券投资策略的证券组合选择模型，研究了它的解的结构、它的有效边界的构成。     

不允许卖空的组合证券投资策略的确定

以Ｍａｒｋｏｗｉｔｚ的均值——方差模型的理论为基础，研究了组合证券投资策略确定过程中的两个主要问题：投资对象的选择方法和风险选择方法     

组合证券投资的有效边界

本文讨论了由Ｍａｒｋｏｗｉｔｚ提出的证券组合模型的边界函数性质．并给出了在变量非负条件下边界函数的确定方法。得出了在非负条件下有效边界函数是预期回收值的逐段二次凸函数以及Ｍａｒｋｏｗｅｉｔｚ模型的最优解是预期回收值的逐段线性向量函数的结论。     

一类证券组合投资选择模型

在分析[1]模型的基础上提出了一类相似的证券组合选择模型。     

含交易费用的证券组合投资的多目标规划模型

以Ｍａｒｋｏｗ ｉｔｚ证券组合投资理论为基础,采用相对偏好参数,建立了含交易费用的证券组合的多目标规划模型,并给出了它的解法及有效边界的确定方法     

含交易费用的投资组合问题的新模型及其求解途径

本文提出了证券投资组合的一个新模型.该模型综合考虑了证券的收益率、证券分红和证券价格的关系,并将证券分红和证券价格作为系统的随机参数处理,建立了证券投资组合的随机规划模型.利用机会约束规划方法,我们研究了将所建立的随机规划模型转化为普通光滑优化问题求解的方法,得到了该类问题求解的有效途径.     

证券投资组合理论的一种新模型及其应用

马科维茨(Markowitz)以证券收益率的方差作为投资风险的测度建立了组合证券投资模型，本基于熵的概念，在研究马科维茨(Markowitz)证券投资组合模型的基础上，分析了该模型用方差度量风险的不足，进而提出一种新的证券投资组合优化模型，并以实例作了说明。     

熵—证券投资组合风险的一种新的度量方法

本文在研究马科维茨 ( Markowitz)证券投资组合模型的基础上 ,分析了该模型用方差度量风险的缺陷 ,进而提出用熵作为风险的度量方法 ,改进马科维茨 ( Markowitz)证券投资组合模型 ,并建立新的证券投资组合优化模型     

均值-叉熵证券投资组合优化模型

在研究马科维茨(Markowitz)证券投资组合模型的基础上,分析了该模型用方差度量风险的缺陷,进而提出用叉熵作为风险的度量方法,建立了均值-叉熵的投资组合优化模型.该模型计算简便,更易被一般投资人所使用.     

上证指数、深圳指数预测的马尔柯夫链预测模糊模型

介绍马尔柯夫链预测法的模糊模型 ,应用模糊模型对证券指数进行预测的实证研究     

Mathematical models of recognition and confusion in psychology

A wide range of mathematical models of recognition and confusion in psychological experiments is examined. A taxonomy for the classification of these models is proposed. This taxonomy includes discriminant models, feature-confusion models, sophisticated guessing models and choice models. Both the within-class and between-class relationships of these models are examined so as to provide a framework for the application of these models to theories of human pattern recognition. Where appropriate, specific areas of application of these models in the context of human perceptual processing are presented and discussed.     

Convexity preserving jump-diffusion models for option pricing

We investigate which jump-diffusion models are convexity preserving. The study of convexity preserving models is motivated by monotonicity results for such models in the volatility and in the jump parameters. We give a necessary condition for convexity to be preserved in several-dimensional jump-diffusion models. This necessary condition is then used to show that, within a large class of possible models, the only convexity preserving models are the ones with linear coefficients.     

Regression based reserving models and partial information

We consider a class of distribution-free regression models only defined in terms of moments, which can be used to model separate reported but not settled reserves, and incurred but not reported reserves. These regression models can be estimated using standard least squares and method of moments techniques, similar to those used in the distribution-free chain-ladder model. Further, these regression models are closely related to double chain-ladder type models, and the suggested estimation techniques could serve as alternative estimation procedures for these models. Due to the simple structure of the models it is possible to obtain Mack-type mean squared error of prediction estimators. Moreover, the analysed regression models can be used on different levels of detailed data, and by using the least squares estimation techniques it is possible to show that the precision in the reserve predictor is improved by using more detailed data. These regression models can be seen as a sequence of linear models, and are therefore also easy to bootstrap non-parametrically.     

Seasonal Space–Time Models for Climate Systems

A class of seasonal space–time models for general lattice systems is proposed. Covariance properties of spatial first-order models are studied. Estimation approaches in time series analysis are adopted and forecasting techniques using the seasonal space–time models are discussed. The models are applied to 516 consecutive fields of monthly averaged 500 mb geopotential heights over a 10 × 10 lattice in the extra-tropical northern hemisphere for the purpose of understanding the underlying statistical structure. It is found that space–time models with instantaneous spatial component give the best fit compared to other models in terms of maximizing the conditional likelihood function. The models are potentially useful for assessing the consistency of outputs from laboratory-based numerical models with field observations. Forecasting ability of the seasonal space–time models is also investigated. This revised version was published online in June 2006 with corrections to the Cover Date.     

Some relationships between fuzzy and random set-based classifiers and models

When designing rule-based models and classifiers, some precision is sacrificed to obtain linguistic interpretability. Understandable models are not expected to outperform black boxes, but usually fuzzy learning algorithms are statistically validated by contrasting them with black-box models. Unless performance of both approaches is equivalent, it is difficult to judge whether the fuzzy one is doing its best, because the precision gap between the best understandable model and the best black-box model is not known.In this paper we discuss how to generate probabilistic rule-based models and classifiers with the same structure as fuzzy rule-based ones. Fuzzy models, in which features are partitioned into linguistic terms, will be compared to probabilistic rule-based models with the same number of terms in every linguistic partition. We propose to use these probabilistic models to estimate a lower precision limit which fuzzy rule learning algorithms should surpass.     

Harvesting discrete nonlinear age and stage structured populations

A natural extension of age structured Leslie matrix models is to replace age classes with stage classes and to assume that, in each time period, the transition from one stage class to the next is incomplete; that is, diagonal terms appear in the transition matrix. This approach is particularly useful in resource systems where size is more easily measured than age. In this linear setting, the properties of the models are known; and these models have been applied to the analysis of population problems. A more applicable setting is to assume that the reproduction, survival, and transition parameters in the model are density dependent. The behavior of such models is determined by the form of this density dependence. Here, we focus on models in which the parameters depend on the value of an aggregated variable, defined to be the weighted sum of the number of individuals in each stage class. In forestry models, for example, this aggregated variable may represent a basal area index; in fisheries models, it may represent a spawning stock biomass. Current age structured nonlinear stock-recruitment fisheries models are a special case of the models considered here. Certain results that apply to age structured models can be extended to this broader class of models. In particular, the questions addressed relate to the minimum number of age classes that need to be harvested to obtain maximum sustainable yield policies and to managing resources under nonequilibrium and stochastic conditions. Application of the model to problems in fisheries, forestry, pest, and wildlife management is also discussed.The author would like to thank R. G. Haight for comments and discussions relating to the material presented here. This work was supported by NSF Grant DMS-85-11717.     

SOME ASYMPTOTIC INFERENCE IN QUASI-LIKELIHOOD NONLINEAR MODELS:A GEOMETRIC APPROACH

§ 1　IntroductionIt is well known that quasi-likelihood models introduced by Wedderburn[1 ] greatlywiden the scope of generalized linear models by using a much weaker assumption in whichonly the firstand second moments ofresponse vector Yare needed to replace the full distri-butional assumption about Y in the models.It has drawn considerable attention in recentliterature(e.g.see[2～ 6] and so on) .However,little work has been done on the issuefrom a geometric viewpoint.The purpose of this p…     

拟似然非线性模型的某些渐近推断:几何方法

Abstract. A modified Bates and Watts geometric framework is proposed for quasi-likelihoodnonlinear models in Euclidean inner product space. Based on the modified geometric framework,some asymptotic inference in terms of curvatures for quasi-likelihood nonlinear models is stud-ied. Several previous results for nonlinear regression models and exponential family nonlinearmodels etc. are extended to quasi-likelihood nonlinear models.     

Stochastic actor‐oriented models for network change

A class of models is proposed for longitudinal network data. These models are along the lines of methodological individualism: actors use heuristics to try to achieve their individual goals, subject to constraints. The current network structure is among these constraints. The models are continuous time Markov chain models that can be implemented as simulation models. They incorporate random change in addition to the purposeful change that follows from the actors’ pursuit of their goals, and include parameters that must be estimated from observed data. Statistical methods are proposed for estimating and testing these models. These methods can also be used for parameter estimation for other simulation models. The statistical procedures are based on the method of moments, and use computer simulation to estimate the theoretical moments. The Robbins‐Monro process is used to deal with the stochastic nature of the estimated theoretical moments. An example is given for Newcomb's fraternity data, using a model that expresses reciprocity and balance.     

基于制造商技术创新水平的古诺与斯坦伯格博弈对比分析

研究了不同博弈模型下制造商产品策略的问题,建立并求解制造商在古诺博弈和斯坦伯格博弈下的模型。通过逆推法求解最优决策,进一步给出了两种博弈模型下的均衡解,并将结果加以对比,得出两种博弈模型对供应商的批发价格和利润、制造商的产品质量和利润的影响。研究表明:供应商的批发价格与博弈模型无关,只和市场规模有关;供应商的利润、制造商的产品质量和利润不仅与博弈模型相关还与制造商的技术创新水平有关。