预期理论与商品价格的形成

本文介绍了四种不同的预期模型即蛛网模型、外推预期模型、适应性预期模型以及理性预期模型 ,说明了预期对商品价格形成的影响 ,并分析了以上四种模型的联系和差异     

认股权证的定价研究

本文分析了认股权证价格的影响因素,运用Black-Scholes的期权定模型,推导出认股权证的定价模型,得到了认股权证的定价模型的几个性质,并探讨了认股权证定价模型的具体应用.     

改进的以x(1)(n)为初始条件的GM模型

在对以为初始条件的GM模型分析的基础之上,初值的选取对模型的精度有着重要的影响。文章在分析了GM(1,1)模型的预测精度之后,提出了一种修正初值法来提高模型的精度,对以为初始条件的GM模型进行了改进,使所建的模型的精度大为提高,最后通过具体的实例验证了模型的实用性与可靠性。     

确定项目工期的网络计划模型的对比分析

文章在对经典CPM/PERT网络计划模型、概率型随机网络计划模型、因素型随机网络计划模型介绍的基础上,就模型的使用条件、工序工期的表示、不确定性因素的量化、模型的使用效果等方面进行了深入的对比剖析.并通过用VisualStudio.NET高级语言编制程序,实现了三种模型的计算功能.最后,通过一个例子对三种模型的计算结果进行了对比分析,结果表明因素型模型具有更大的灵活性和可靠性.     

自相关性的D-W检验与模型的改进

本文针对一阶自相关性的D-W检验方法的弊端,讨论了局部调整模型,利用模型的改进,以及局部调整模型的自身优势,对我国货币流通量模型,选择正确的模型,从而使模型的自相关的程度减轻。估计的结果也很好地反映了贷款额和反映物价水平的居民消费价格指数对我国货币流通量的短期和长期的影响的情况。     

时间序列ARFIMA模型的贝叶斯预测分析

ARFIMA模型是时间序列分析理论体系中的一个新领域,其模型结构比较复杂.本文系统地研究了时间序列ARFIMA(p,d,q)模型的贝叶斯预测问题,给出了模型的似然函数形式,构造了模型参数的先验分布;根据贝叶斯定理严密地推断了参数的后验边缘分布密度函数,建立了贝叶斯ARFIMA模型预测的基本程序,并且进行了实证研究分析.     

NHPP类软件可靠性模型的扩展

时现有的NHPP类软件可靠性模型进行了扩展,提出了一个新NHPP类软倬可靠性的模型,该模型将排除的软件错误数和引入的软件错误数都看成随机变量,并利用该模型对软件的可靠性指标进行评估,最后通过实例对一个特殊的NHPP模型进行了验证.     

企业绩效评价的空间混合效应计量模型构建

文章在对企业业绩评估维度特征分析的基础上,借鉴传统计量检验模型,通过引入空间混合效应模型,构建了企业业绩评估空间结构混合效应的固定模型与随机系数模型,探究了该模型的空间统计检验变量GMM参数;最后就这些计量检验模型及参数进行了讨论,提出了企业业绩空间计量检验模型改进方向,为企业绩效评价工作提供了一个新的研究方向。     

基于企业价值评估的DCF与RIV定价模型的比较

文章运用中国资本市场的截面数据,实证检验了DCF定价模型与RIV定价模型在企业价值评估时的适用性,并对两种方法进行了比较.研究发现:DCF定价模型和RIV定价模型评估企业价值的结果与企业的市场价值呈正相关关系,这两个模型的企业价值的评估结果对企业的市场价值均有显著的解释能力,可以有效地评估企业的价值.但是RIV模型比DCF模型具有更强的解释能力,RIV模型对企业价值评估的结果优于DCF模型的评估结果.     

三种灰色系统模型的预测比较

灰色系统模型误差直接影响模型的预测精度。文章在传统灰色系统模型的基础上,重新定义了误差数列q(0)(k),并对误差数列q(0)(k)进行了深入讨论,从而得到了两个修正模型,并与传统模型进行了比较。     

Convergence properties of the EM algorithm in constrained parameter spaces

The established general results on convergence properties of the EM algorithm require the sequence of EM parameter estimates to fall in the interior of the parameter space over which the likelihood is being maximized. This paper presents convergence properties of the EM sequence of likelihood values and parameter estimates in constrained parameter spaces for which the sequence of EM parameter estimates may converge to the boundary of the constrained parameter space contained in the interior of the unconstrained parameter space. Examples of the behavior of the EM algorithm applied to such parameter spaces are presented.     

Parameter Orthogonality and Bias Adjustment for Estimating Functions

Abstract.  We consider an extended notion of parameter orthogonality for estimating functions, called nuisance parameter insensitivity, which allows a unified treatment of nuisance parameters for a wide range of methods, including Liang and Zeger's generalized estimating equations. Nuisance parameter insensitivity has several important properties in common with conventional parameter orthogonality, such as the nuisance parameter causing no loss of efficiency for estimating the interest parameter, and a simplified estimation algorithm. We also consider bias adjustment for profile estimating functions, and apply the results to restricted maximum likelihood estimation of dispersion parameters in generalized estimating equations.     

Bias reduction in the estimation of a shape second-order parameter of a heavy-tailed model

In extreme value theory, the shape second-order parameter is a quite relevant parameter related to the speed of convergence of maximum values, linearly normalized, towards its limit law. The adequate estimation of this parameter is vital for improving the estimation of the extreme value index, the primary parameter in statistics of extremes. In this article, we consider a recent class of semi-parametric estimators of the shape second-order parameter for heavy right-tailed models. These estimators, based on the largest order statistics, depend on a real tuning parameter, which makes them highly flexible and possibly unbiased for several underlying models. In this article, we are interested in the adaptive choice of such tuning parameter and the number of top order statistics used in the estimation procedure. The performance of the methodology for the adaptive choice of parameters is evaluated through a Monte Carlo simulation study.     

Some Geometric Methods for Constructing Decision Criteria Based On Two-Dimensional Parameters

This paper reviews two types of geometric methods proposed in recent years for defining statistical decision rules based on 2-dimensional parameters that characterize treatment effect in a medical setting. A common example is that of making decisions, such as comparing treatments or selecting a best dose, based on both the probability of efficacy and the probability toxicity. In most applications, the 2-dimensional parameter is defined in terms of a model parameter of higher dimension including effects of treatment and possibly covariates. Each method uses a geometric construct in the 2-dimensional parameter space based on a set of elicited parameter pairs as a basis for defining decision rules. The first construct is a family of contours that partitions the parameter space, with the contours constructed so that all parameter pairs on a given contour are equally desirable. The partition is used to define statistical decision rules that discriminate between parameter pairs in term of their desirabilities. The second construct is a convex 2-dimensional set of desirable parameter pairs, with decisions based on posterior probabilities of this set for given combinations of treatments and covariates under a Bayesian formulation. A general framework for all of these methods is provided, and each method is illustrated by one or more applications.     

Discrete regularized discriminant analysis

A method of regularized discriminant analysis for discrete data, denoted DRDA, is proposed. This method is related to the regularized discriminant analysis conceived by Friedman (1989) in a Gaussian framework for continuous data. Here, we are concerned with discrete data and consider the classification problem using the multionomial distribution. DRDA has been conceived in the small-sample, high-dimensional setting. This method has a median position between multinomial discrimination, the first-order independence model and kernel discrimination. DRDA is characterized by two parameters, the values of which are calculated by minimizing a sample-based estimate of future misclassification risk by cross-validation. The first parameter is acomplexity parameter which provides class-conditional probabilities as a convex combination of those derived from the full multinomial model and the first-order independence model. The second parameter is asmoothing parameter associated with the discrete kernel of Aitchison and Aitken (1976). The optimal complexity parameter is calculated first, then, holding this parameter fixed, the optimal smoothing parameter is determined. A modified approach, in which the smoothing parameter is chosen first, is discussed. The efficiency of the method is examined with other classical methods through application to data.     

Tests for the skewness parameter of two-piece double exponential distribution in the presence of nuisance parameters

In this paper, tests for the skewness parameter of the two-piece double exponential distribution are derived when the location parameter is unknown. Classical tests like Neyman structure test and likelihood ratio test (LRT), that are generally used to test hypotheses in the presence of nuisance parameters, are not feasible for this distribution since the exact distributions of the test statistics become very complicated. As an alternative, we identify a set of statistics that are ancillary for the location parameter. When the scale parameter is known, Neyman–Pearson's lemma is used, and when the scale parameter is unknown, the LRT is applied to the joint density function of ancillary statistics, in order to obtain a test for the skewness parameter of the distribution. Test for symmetry of the distribution can be deduced as a special case. It is found that power of the proposed tests for symmetry is only marginally less than the power of corresponding classical optimum tests when the location parameter is known, especially for moderate and large sample sizes.     

The particle filter based on random number searching algorithm for parameter estimation

This article addresses the issue of parameter estimation in linear system in the presence of Gaussian noises, under which the random number searching algorithm (LJ (Luus and Jaakola) algorithm) is combined with the Rao-Blackwellised particle filter (RBPF) algorithm. This yields the so-called RBPF algorithm based on LJ (RBPF-LJ). Unlike the mature alternatives of generic particle filter, the parameter particles of RBPF-LJ are set as random numbers that search in the parameter value scope, which is regulated based on the estimation result to track the changes of the unknown parameter. The contrasting simulations show that the proposed RBPF-LJ outperform the RBPF as well as the particle filter based on kernel smoothing contraction algorithm on the estimation of the dynamically linear or nonlinear parameter and it can obtain the similar estimation results on the static parameter if some coefficients are regulated.     

Selection of the Ridge Parameter Using Mathematical Programming

Ridge regression solves multicollinearity problems by introducing a biasing parameter that is called ridge parameter; it shrinks the estimates and their standard errors in order to reach acceptable results. Selection of the ridge parameter was done using several subjective and objective techniques that are concerned with certain criteria. In this study, selection of the ridge parameter depends on other important statistical measures to reach a better value of the ridge parameter. The proposed ridge parameter selection technique depends on a mathematical programming model and the results are evaluated using a simulation study. The performance of the proposed method is good when the error variance is greater than or equal to one; the sample consists of 20 observations, the number of explanatory variables in the model is 2, and there is a very strong correlation between the two explanatory variables.     

A method of moments estimator of tail dependence in meta-elliptical models

A meta-elliptical model is a distribution function whose copula is that of an elliptical distribution. The tail dependence function in such a bivariate model has a parametric representation with two parameters: a tail parameter and a correlation parameter. The correlation parameter can be estimated by robust methods based on the whole sample. Using the estimated correlation parameter as plug-in estimator, we then estimate the tail parameter applying a modification of the method of moments approach proposed in the paper by Einmahl et al. (2008). We show that such an estimator is consistent and asymptotically normal. Further, we derive the joint limit distribution of the estimators of the two parameters. We illustrate the small sample behavior of the estimator of the tail parameter by a simulation study and on real data, and we compare its performance to that of the competitive estimators.     

Bayesian inference for truncated exponential distributions

Truncated normal distributions are considered as prior distributions for the truncation parameter in truncated exponential models. Posterior istributions re obtained, and inferenceforthe truncation parameter and for the reliability function is discussed. One and two parameter models are considered.