Oracle Inequalities for Convex Loss Functions with Nonlinear Targets

This article considers penalized empirical loss minimization of convex loss functions with unknown target functions. Using the elastic net penalty, of which the Least Absolute Shrinkage and Selection Operator (Lasso) is a special case, we establish a finite sample oracle inequality which bounds the loss of our estimator from above with high probability. If the unknown target is linear, this inequality also provides an upper bound of the estimation error of the estimated parameter vector. Next, we use the non-asymptotic results to show that the excess loss of our estimator is asymptotically of the same order as that of the oracle. If the target is linear, we give sufficient conditions for consistency of the estimated parameter vector. We briefly discuss how a thresholded version of our estimator can be used to perform consistent variable selection. We give two examples of loss functions covered by our framework.     

In-Sample Inference and Forecasting in Misspecified Factor Models

This article considers in-sample prediction and out-of-sample forecasting in regressions with many exogenous predictors. We consider four dimension-reduction devices: principal components, ridge, Landweber Fridman, and partial least squares. We derive rates of convergence for two representative models: an ill-posed model and an approximate factor model. The theory is developed for a large cross-section and a large time-series. As all these methods depend on a tuning parameter to be selected, we also propose data-driven selection methods based on cross-validation and establish their optimality. Monte Carlo simulations and an empirical application to forecasting inflation and output growth in the U.S. show that data-reduction methods outperform conventional methods in several relevant settings, and might effectively guard against instabilities in predictors’ forecasting ability.     

Factor screening in nonregular two-level designs based on projection-based variable selection

In this paper, we focus on the problem of factor screening in nonregular two-level designs through gradually reducing the number of possible sets of active factors. We are particularly concerned with situations when three or four factors are active. Our proposed method works through examining fits of projection models, where variable selection techniques are used to reduce the number of terms. To examine the reliability of the methods in combination with such techniques, a panel of models consisting of three or four active factors with data generated from the 12-run and the 20-run Plackett–Burman (PB) design is used. The dependence of the procedure on the amount of noise, the number of active factors and the number of experimental factors is also investigated. For designs with few runs such as the 12-run PB design, variable selection should be done with care and default procedures in computer software may not be reliable to which we suggest improvements. A real example is included to show how we propose factor screening can be done in practice.     

Bayesian variable selection approach to a Bernstein polynomial regression model with stochastic constraints

This paper provides a Bayesian estimation procedure for monotone regression models incorporating the monotone trend constraint subject to uncertainty. For monotone regression modeling with stochastic restrictions, we propose a Bayesian Bernstein polynomial regression model using two-stage hierarchical prior distributions based on a family of rectangle-screened multivariate Gaussian distributions extended from the work of Gurtis and Ghosh [7 S.M. Curtis and S.K. Ghosh, A variable selection approach to monotonic regression with Bernstein polynomials, J. Appl. Stat. 38 (2011), pp. 961–976. doi: 10.1080/02664761003692423[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]]. This approach reflects the uncertainty about the prior constraint, and thus proposes a regression model subject to monotone restriction with uncertainty. Based on the proposed model, we derive the posterior distributions for unknown parameters and present numerical schemes to generate posterior samples. We show the empirical performance of the proposed model based on synthetic data and real data applications and compare the performance to the Bernstein polynomial regression model of Curtis and Ghosh [7 S.M. Curtis and S.K. Ghosh, A variable selection approach to monotonic regression with Bernstein polynomials, J. Appl. Stat. 38 (2011), pp. 961–976. doi: 10.1080/02664761003692423[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]] for the shape restriction with certainty. We illustrate the effectiveness of our proposed method that incorporates the uncertainty of the monotone trend and automatically adapts the regression function to the monotonicity, through empirical analysis with synthetic data and real data applications.     

信任危机、逆向选择与品牌重建——来自网贷之家的经验证据

以中国P2P行业信任危机为背景,借助网贷之家公布的数据,利用面板固定效应模型研究信任缺失情形下的逆向选择问题以及政府和行业协会重建品牌信任的效果,结果表明:P2P行业信任危机下的平台旨在通过建设品牌形象来吸引投资人与借款人的做法是徒劳的,信任缺失导致合规平台的诚信经营并不为公众所认可,长此以往这些平台很可能会退出市场,进而引发逆向选择问题;自上而下的制度建设在重建品牌信任中取得了可喜的成绩,有效缓解并逆转了P2P行业的逆向选择问题,且市场整治初见成效。此研究成果对于目前正在进行的P2P行业整顿具有十分现实的指导意义。     

资源禀赋、电商认知与家庭农场主电子商务采纳行为

基于河北、江西和云南3省893个家庭农场调研数据,研究了资源禀赋、电商认知、政府扶持对家庭农场主电子商务采纳行为的影响机理。结果表明:资源禀赋中的家庭农场主受教育程度、电商培训和农产品特色明显程度对其电子商务行为采纳及采纳程度有显著正向影响;年龄、注册品牌、绿色或有机认证、产品深加工对采纳程度有显著正向影响;组织化和电商认知对电子商务采纳行为有显著正向影响;政府扶持对电子商务行为采纳具有显著正向影响,且对资源禀赋、电商认知 电子商务行为采纳及采纳程度关系中有正向调节效应。在此基础上,提出培育农产品电商品牌,提升农场主电商运营能力,创新电商经营模式和提高家庭农场组织化程度等建议。     

商业银行利率选择与信贷配给问题研究

目前我国经济高速发展,但融资难的问题一直阻碍中小企业的进一步发展,这是多方原因造成的.其中长期存在于信贷市场中的信贷配给(credit rationing)现象、银行利率选择问题一直为国内学术界所关注,但是对于形成信贷配给的银行与企业的决策过程关注得不多.笔者从传统的信贷市场均衡入手,着重分析了信贷市场的利率选择、最优利率形成原则以及信贷配给产生的原因,以期对我国进行宏观经济调整起到一定的借鉴作用.     

基于熵权法的财务危机预警指标选择研究

合理选择财务指标是财务危机预警研究中的重要内容,用定性和定量相结合的方法进行选择是科学有效的方法之一。首先根据初选原则对财务指标进行了初步选择,然后提出了一种基于熵权的变量选择方法,对初选的财务指标进行定量筛选,进行实证研究,最终确定了应用于财务危机预警的财务指标。     

行政判例视野中的行政法原则研究——以梁宝福案、赖恒安案为例评释

行政法原则在行政执法实践和司法审判实践中具有重要意义,而行政判例中则蕴含着丰富的行政法原则.以梁宝富案关于合法性审查和保护诉权及赖恒安案关于成熟原则的行政判例为例来看,通过行政判例来运用、发展、创制行政法原则,对于推进我国行政法治有着极其重要的意义.     

《论语》中的修养之道——从文本形式进入的思考

《论语》记载了孔子的言行及其与弟子时人的问答,解读时宜把语录意义与语录存在的具体语境、涉及的特定对象联系起来;假如脱离具体境况纯粹作形而上的认识,就不免有断章取义之嫌。语录涉及的对象大略分为"君"、"仕"和"士"三类,其中的修养之道各有侧重:关于"君",要求仁爱、以身作则;关于"仕",要求敬事、谨慎、谦恭和识时务;关于"士",则要求有信仰和批判的精神,奋发有为。孔子提出这样的修养之道,是根据角色定位("角色")和适时而处("时中")的原则,其中关于个体性和社会性相统一的认识尤其有当代意义。