Bertrand竞争下融资策略与产品差异化策略的博弈分析

融资策略与产品差异化策略是企业两大重要的决策.本文通过构造一个纵向产品差异化的三阶段动态博弈模型,研究了不确定环境下,企业债务融资对产品差异化策略和定价策略的影响,并与不存在债务策略下的均衡结果进行了比较.研究表明,负债情形下,企业的均衡质量和均衡价格均高于无负债下的质量和价格,产品纵向差异度低于无负债下的差异度,且负债水平越高,企业的均衡质量、均衡价格和均衡利润越高,企业间的产品纵向差异度也越高.     

基于方差分析的资本结构决策模型

本文利用统计学原理与风险决策分析方法,提出了资本结构决策新的方差分析方法和矩阵模型.与原有的基于概率分析的方法比较,本文建立的模型方法克服和舍弃了模糊性和难操作性,有效地权衡了风险与收益,使资本结构决策模型更富可操作性和广泛适用性,能够为企业最优资本结构决策提供一定的决策依据.     

双寡头垄断市场的价格竞争与产品差异化策略——一个博弈论模型及其扩展

分析三阶段Bertrand-Stackelberg市场价格竞争与产品差异化选址策略,将之与Bertrand-Nash市场均衡进行静态比较;其次,构造一个具体的数字例子,演示随着产品差异化程度的提高,双寡头垄断市场中企业均衡利润的变动规律.在此基础上,识别经典的产品差异化定位博弈模型的各种假设条件,并在放松假设以讨论存在边际成本优势、消费者选择行为以及重复动态博弈的情形下,寡头垄断市场中的企业均衡利润和均衡价格的变化趋势及其稳定性问题.放松传统模型的假设条件有助于完整地理解和分析许多长期被孤立研究的产业竞争问题.     

一种基于可能度的区间判断矩阵排序法

给出了区间数两两比较的一种可能度公式,以及文献[4]中的方法参数k,m的取值范围,提出了一种基于可能度的区间判断矩阵排序方法。最后,通过算例说明了该法的可行性和有效性。     

The Incremental Group Testing Model for Gap Closing in Sequencing Long Molecules

In this paper, we propose the incremental group testing model for the gap closing problem, which assumes that we can tell the difference between the outcome of testing a subset S, and the outcome of testing S {x}. We also give improvements over currently best results in literature for some other models.     

The Steiner Tree Problem in Kalmanson Matrices and in Circulant Matrices

We investigate the computational complexity of two special cases of the Steiner tree problem where the distance matrix is a Kalmanson matrix or a circulant matrix, respectively. For Kalmanson matrices we develop an efficient polynomial time algorithm that is based on dynamic programming. For circulant matrices we give an -hardness proof and thus establish computational intractability.     

Computing and approximating multivariate chi-square probabilities

We consider computational methods for evaluating and approximating multivariate chi-square probabilities in cases where the pertaining correlation matrix or blocks thereof have a low factorial representation. To this end, techniques from matrix factorization and probability theory are applied. We outline a variety of statistical applications of multivariate chi-square distributions and provide a system of MATLAB programs implementing the proposed algorithms. Computer simulations demonstrate the accuracy and the computational efficiency of our methods in comparison with Monte Carlo approximations, and a real data example from statistical genetics illustrates their usage in practice.     

Confidence interval estimation and hypothesis testing for a common correlation coefficient

Several procedures have been proposed for testing the hypothesis that all off-diagonal elements of the correlation matrix of a multivariate normal distribution are equal. If the hypothesis of equal correlation can be accepted, it is then of interest to estimate and perhaps test hypotheses for the common correlation. In this paper, two versions of five different test statistics are compared via simulation in terms of adequacy of the normal approximation, coverage probabilities of confidence intervals, control of Type I error, and power. The results indicate that two test statistics based on the average of the Fisher z-transforms of the sample correlations should be used in most cases. A statistic based on the sample eigenvalues also gives reasonable results for confidence intervals and lower-tailed tests.     

Statistical Estimates for Modified Gravitation Model by Aggregated Data

A nonlinear regression model for forecasting of passenger flow between various spatial points (towns) is described. Unknown parameters are estimated using aggregated data when the information about a number of the departed passengers from each town is available only. For estimation, the least squares and maximum likelihood methods are used. Numerical examples are performed to illustrate the proposed approaches.     

Numerical evaluation of tests based on different heteroskedasticity-consistent covariance matrix estimators

This article considers the issue of performing tests in linear heteroskedastic models when the test statistic employs a consistent variance estimator. Several different estimators are considered, namely: HC0, HC1, HC2, HC3, and their bias-adjusted versions. The numerical evaluation is performed using numerical integration methods; the Imhof algorithm is used to that end. The results show that bias-adjustment of variance estimators used to construct test statistics delivers more reliable tests when they are performed for the HC0 and HC1 estimators, but the same does not hold for the HC3 estimator. Overall, the most reliable test is the HC3-based one.