Asymptotic properties of the maximum-likelihood estimator in zero-inflated binomial regression

The zero-inflated binomial (ZIB) regression model was proposed to account for excess zeros in binomial regression. Since then, the model has been applied in various fields, such as ecology and epidemiology. In these applications, maximum-likelihood estimation (MLE) is used to derive parameter estimates. However, theoretical properties of the MLE in ZIB regression have not yet been rigorously established. The current paper fills this gap and thus provides a rigorous basis for applying the model. Consistency and asymptotic normality of the MLE in ZIB regression are proved. A consistent estimator of the asymptotic variance–covariance matrix of the MLE is also provided. Finite-sample behavior of the estimator is assessed via simulations. Finally, an analysis of a data set in the field of health economics illustrates the paper.     

Multiplicative consistency analysis for interval fuzzy preference relations: A comparative study

To examine the multiplicative consistency of interval fuzzy preference relations (IFPRs), this paper first analyzes the limitations associated with the previous consistency concepts. Accordingly, a new consistency concept is defined that is an extension of the crisp case and overcomes limitations in the previous concepts. Next, a linear programming model to judge the consistency of IFPRs is constructed, and an approach to derive multiplicative consistent IFPRs is introduced. Furthermore, goal-programming models to determine missing values in an incomplete IFPR are constructed that have the highest consistent level with respect to known values. Moreover, a multiplicative consistency and consensus based method for group decision making with IFPRs is developed that can address incomplete and inconsistent cases. Finally, two practical decision-making problems are offered to demonstrate the feasibility and efficiency of the new method, and an analysis of a numerical and theoretical comparison with several related methods is performed.     

上市公司信用风险度量模型的实证比较研究

本文首次运用Merton模型和Leland-Toft模型对我国上市公司的信用风险进行了比较实证研究。研究结果表明,Merton模型对上市公司信用风险的预期违约率的计算明显太低。但是,违约距离的计算在一定程度上可以反映出不同公司信用风险存在的差异。而使用Leland-Toft模型计算信用风险的预期违约率较Merton模型有更高的敏感性、有效性。依据大公国际资信评级体系评级的结果,将A股50指数上市公司的信用评级等级,对比Leland-Toft模型计算的预期违约率,不同等级的上市公司,其预期违约率有明显的不同,说明其模型的预测是有效的。并且,也反映出近期预期违约率低而远期则较高的规律性。但是,计算的预期违约率还是偏低,说明Leland-Toft模型在用于目前的上市公司信用风险分析时,尚需调整有关的参数。     

Optimal Contracts when Enforcement is a Decision Variable

This paper analyzes choice‐theoretic costly enforcement in an intertemporal contracting model with a differentially informed investor and entrepreneur. An intertemporal contract is modeled as a mechanism in which there is limited commitment to payment and enforcement decisions. The goal of the analysis is to characterize the effect of choice‐theoretic costly enforcement on the structure of optimal contracts. The paper shows that simple debt is the optimal contract when commitment is limited and costly enforcement is a decision variable (Theorem 1). In contrast, stochastic contracts are optimal when agents can commit to the ex‐ante optimal decisions (Theorem 2). The paper also shows that the costly state verification model can be viewed as a reduced form of an enforcement model in which agents choose payments and strategies as part of a perfect Bayesian Nash equilibrium.     

基于模糊判断矩阵信息确定专家权重的方法

本文对群组模糊判断矩阵集结过程中确定专家权重的问题进行了研究,建立了模糊判断矩阵的特征矩阵和求解群集结矩阵的最优化模型,通过矩阵之间距离度量判断信息自身逻辑一致性程度和群体相容性程度,给出一种基于专家判断信息的可信度计算其后验权重的方法,最后用算例予以说明.     

区间数互补判断矩阵的一致性及其排序研究

由于目前国内外文献对区间数互补判断矩阵的性质研究较少,从而使得对排序方法的相关研究缺乏理论依据.针对这些缺陷,本文研究了区间数互补判断矩阵的性质及其排序方法问题.根据区间数互补判断矩阵的定义,给出了区间数互补判断矩阵的一致性、严格强传递性与弱传递性等定义,并研究了一致性判断矩阵的性质,并说明这些性质更符合人们的思维特征.在一致性性质的基础上建立了区间数互补判断矩阵排序的非线性规划模型,算例分析表明该方法是有效可行的.     

Aggregating classifiers via Rademacher–Walsh polynomials

Methods are proposed to combine several individual classifiers in order to develop more accurate classification rules. The proposed algorithm uses Rademacher–Walsh polynomials to combine M (≥2) individual classifiers in a nonlinear way. The resulting classifier is optimal in the sense that its misclassification error rate is always less than, or equal to, that of each constituent classifier. A number of numerical examples (based on both real and simulated data) are also given. These examples demonstrate some new, and far-reaching, benefits of working with combined classifiers.     

Consistent estimation of parameters and quantiles of the three-parameter gamma distribution based on Type-II right-censored data

In this paper, we propose a method of estimation of parameters and quantiles of the three-parameter gamma distribution based on Type-II right-censored data. In the proposed method, under mild conditions, the estimates always exist uniquely, and the estimators have consistency over the entire parameter space. Through Monte Carlo simulations, we further show that the proposed method performs well compared with another prominent method of estimation in terms of bias and root mean-squared error in small-sample situations. Finally, two real data sets are used for illustrating the proposed method.     

Existence,uniqueness and consistency of estimation of life characteristics of three-parameter Weibull distribution based on Type-II right censored data

In this paper, we propose a new method of estimation for the parameters and quantiles of the three-parameter Weibull distribution based on Type-II right censored data. The method, based on a data transformation, overcomes the problem of unbounded likelihood. In the proposed method, under mild conditions, the estimates always exist uniquely, and the estimators are also consistent over the entire parameter space. Through Monte Carlo simulations, we further show that the proposed method of estimation performs well compared to some prominent methods in terms of bias and root mean squared error in small-sample situations. Finally, two real data sets are used to illustrate the proposed method of estimation.