一个新的银行信用风险评估方法

本文以银行信用风险管理为例,将粗糙集和决策树两种具有互补优势的数据挖掘方法相结合,对客户信用做出归类分析判断,最后利用决策树生成决策规则.实践证明,这种方法忠于原始数据,提高了分类准确度,减小了决策树规模,具有良好的性能.     

基于KPCA和RS理论的支持向量分类机及其应用研究

核主成分分析KPCA是近年来提出的一个十分有效的数据降维方法,但它并不能保证所提取的第一主成分最适用于降维后的数据分类。粗糙集RS理论是处理这类问题的一个有效方法。提出一个基于KPCA与RS理论的支持向量分类机SVC,利用RS理论和信息熵原理对运用KP（A进行特征提取后的训练样本进行特征选择,保留重要特征,力求减小求解问题的规模,提高SVC的性能。在构建2006年上市公司财务困境预警模型的数值实验中,以KPCA、RS理论作为前置系统的SVC取得了良好效果。     

具有变系数的2n阶中立型微分方程正解的存在性

本文给出具有变系数Ｐ（ｔ）的２ｎ阶中立型微分方程 （ｘ（ｔ）－Ｐ（ｔ）_ｘ（ｔ－τ））~（２ｎ）+ｆ（ｔ,ｘ（ｔ－τ_1（ｔ））,...,ｘ（ｔ－τ_ｍ（ｔ）））＝０正解存在的若干个充分条件．本文结果部分地回答了文［１］提出的问题．     

庄盘珠《秋水轩集》考述

清代乾嘉时期常州女诗人庄盘珠的诗词作品,初有《秋水轩集》、《紫薇轩集》及《莲佩诗草》数种,以抄本形态存在,后《紫薇轩集》、《莲佩诗草》亡佚,《秋水轩集》以诗词分刻或合刊的不同形态流传至今。冒俊编校如不及斋本是目前最为完善的合刊本。     

基于全要素生产率的中国GDP数据准确性评估

 中国GDP数据的准确性评估问题,一直受到国内外各方面的关注。本文从反逻辑思路,考察作为技术进步的替代性指标全要素生产率（TFP）,反观GDP及其增长率是否准确或可信。运用隐性变量法通过建立状态空间模型测算中国1979－2008年间TFP增长率,分析GDP数据是否存在误差。结果表明1981年、1988年和2000年的TFP增长率的下降,找不到非正常情况下外生因素的影响,本文认为这是GDP核算误差造成的结果。     

Variable Royalty Rates for Improving Franchise Channel Coordination*

A business format franchisor obtains a major part of its revenues from franchise royalties, which are typically a fixed percentage of franchisee gross sales. When a fixed royalty rate is used and the marginal costs of operating the franchise are increasing, the franchisee does not have an incentive to increase sales beyond a certain “optimal” volume. We present a model that recommends the use of a variable franchise royalty rate for extending this optimal sales volume. For a general convex cost function, we show that a new lower rate can be applied to incremental sales beyond the original optimal level. We show that this new rate should be less than half of the original rate when a quadratic cost function is applicable. Adopting a variable royalty rate increases franchisor royalty revenues and franchisee profits.     

Bayesian quantile regression and variable selection for partial linear single-index model: Using free knot spline

Partial linear single-index model (PLSIM) has both the flexibility of nonparametric treatment and interpretability of linear term, yet existing literatures about it mainly focused on mean regression, and quantile regression analysis is scarce. Based on free knot spline approximation, we apply asymmetric Laplace distribution to implement Bayesian quantile regression, and perform variable selection in linear term and index vector via binary indicators. Our approach is exempt from regularity conditions in frequentist method, and could execute variable selection and quantile regression under mutual posterior correction, which is also the first work to implement them jointly for PLSIM in fully Bayesian framework. The numerical simulation manifests the superiority of our approach to previous methods, which embodied in better efficiency of variable selection, index vector estimates and link function approximation with different error distributions. For illustration of its application, we build a power consumption model of A₂/O process in wastewater treatment and emphatically analyze the impact of water quality factors.     

Exponentiated generalized Pareto distribution: Properties and applications towards extreme value theory

The GPD is a central distribution in modelling heavy tails in many applications. Applying the GPD to actual datasets however is not trivial. In this paper we propose the Exponentiated GPD (exGPD), created via log-transform of the GPD variable, which has less sample variability. Various distributional quantities of the exGPD are derived analytically. As an application we also propose a new plot based on the exGPD as an alternative to the Hill plot to identify the tail index of heavy tailed datasets, and carry out simulation studies to compare the two.     

Bayesian covariance matrix estimation using a mixture of decomposable graphical models

We present a Bayesian approach to estimating a covariance matrix by using a prior that is a mixture over all decomposable graphs, with the probability of each graph size specified by the user and graphs of equal size assigned equal probability. Most previous approaches assume that all graphs are equally probable. We show empirically that the prior that assigns equal probability over graph sizes outperforms the prior that assigns equal probability over all graphs in more efficiently estimating the covariance matrix. The prior requires knowing the number of decomposable graphs for each graph size and we give a simulation method for estimating these counts. We also present a Markov chain Monte Carlo method for estimating the posterior distribution of the covariance matrix that is much more efficient than current methods. Both the prior and the simulation method to evaluate the prior apply generally to any decomposable graphical model.     

Penalized regression with correlation-based penalty

A new regularization method for regression models is proposed. The criterion to be minimized contains a penalty term which explicitly links strength of penalization to the correlation between predictors. Like the elastic net, the method encourages a grouping effect where strongly correlated predictors tend to be in or out of the model together. A boosted version of the penalized estimator, which is based on a new boosting method, allows to select variables. Real world data and simulations show that the method compares well to competing regularization techniques. In settings where the number of predictors is smaller than the number of observations it frequently performs better than competitors, in high dimensional settings prediction measures favor the elastic net while accuracy of estimation and stability of variable selection favors the newly proposed method.