Estimation of the mean in ranked set sampling with non responses

The estimation of the population mean when ranked set sampling [rss] is used for selecting the sample and non responses [nr] are present, is studied. The nr stratum is sub sampled using simple random sampling with replacement. Two strategies are analyzed. One of them is based on the selection of a sub sample from the nr in each cycle. The other uses sub samples selected among the nr in each rank.  The accuracy of the proposed estimators is characterized by the corresponding expected variances. Simulations and real life data are used for analyzing the behavior of them. Acknowledgements: This paper was developed partially during the visit of the author to Université des Antilles et Gouyane. The author gratefully acknowledges the helpful suggestions of the referees and thanks the support of DAAD for visiting Humboldt University where a version of the paper version was made.     

Stop and Go Agricultural Policies with a Land Market

This article studies the design of farm policy in the presence of asymmetric information about farmers' productivity, a government objective to insure farmers a minimum "parity" income, an endogenous land rent, and diminishing returns on alternative (nonprogram crop) land uses. In this setting, acreage set asides are never part of an optimal farm policy, although compensated acreage limits are. When there are new farmer entrants who cannot be excluded from farm programs, optimal policy takes the form of a pure voluntary acreage limitation—or "buyout"—program in which high-cost producers participate and low-cost producers do not.     

离散选择模型的基本原理及其发展演进评介

离散选择模型的研究真正兴起于19世纪50年代末，属于微观计量经济学的范畴。该模型能够对个体和家庭行为进行经验性的统计分析，因而在经济学和其他社会科学中得到广泛的应用。本文从离散选择模型的基本性质及效用最大化的理论背景出发，指出logit模型虽然使用的是最早并且最为广泛的离散选择模型，但是其存在着三大局限性：不能表示随机口味的变化、暗含成比例的替代形式和不能处理不可观测因素在不同期间相关的情形。GEV（含嵌套logit）、probit和混合logit模型等其他的离散选择模型，很大程度上都是为了避免这些限制而产生并发展起来的。     

基于集对分析法的地区大中型工业企业自主创新能力评价

为了对地区大中型工业企业自主创新能力进行全面、科学的评价,基于集对分析法,提出了地区大中型工业企业自主创新能力评价指标体系及综合评价模型。通过对我国30个省市区大中型工业企业的实证分析,表明该方法科学简便,具有很好的实用价值。     

Forecasting recovery rates on non-performing loans with machine learning

We compare the performance of a wide set of regression techniques and machine-learning algorithms for predicting recovery rates on non-performing loans, using a private database from a European debt collection agency. We find that rule-based algorithms such as Cubist, boosted trees, and random forests perform significantly better than other approaches. In addition to loan contract specificities, predictors that refer to the bank recovery process — prior to the portfolio’s sale to a debt collector — are also shown to enhance forecasting performance. These variables, derived from the time series of contacts to defaulted clients and client reimbursements to the bank, help all algorithms better identify debtors with different repayment ability and/or commitment, and in general those with different recovery potential.     

On the non-emptiness of the Mas-Colell bargaining set

We introduce an extension of the Mas-Colell bargaining set and construct, by an elaboration on a voting paradox, a superadditive four-person non-transferable utility game whose extended bargaining set is empty. It is shown that this extension constitutes an upper hemicontinuous correspondence. We conclude that the Mas-Colell bargaining set of a non-levelled superadditive NTU game may be empty.     

Small dimension PDE for discrete Asian options

This paper presents an efficient method for pricing discrete Asian options in presence of smile and non-proportional dividends. Using an homogeneity property, we show how to reduce an n0 dimensional problem to a one- or two-dimensional one. We examine different numerical specifications of our dimension reduced PDE using a Crank–Nicholson method (interpolation method, grid boundaries, time and space steps) as well as the extension to the case of non-proportional discrete dividends, using a jump condition. We benchmark our results with Quasi Monte-Carlo simulation and a multi-dimensional PDE     

一种基于佳点集遗传算法的物流配送网络优化

首先根据实际问题分析了物流配送网络优化模型的各个关键组成部分,包括优化目标、决策变量和约束条件,并针对目前物流配送网络优化算法中存在的一些问题提出了一种新的算法,其核心是佳点集遗传算法。该算法编码采用prufer num ber结构,变异和交叉概率自适应选择。     

集值函数及向量值函数的两类线性算子逼近

本文得到了利用插值型线性算子列及积分型算子列逼近连续集值函数及连续向量值函数的收敛逼近阶。     

项目团队成员绩效考评研究

项目团队成员绩效考评是项目人力资源管理的关键环节之一，是整个人力资源管理的基础。科学、公正、合理的绩效考评，对团队成员的激励、薪酬、晋升都具有重要意义。引入模糊综合考评理论，在阶段性考评的基础上着眼于对项目成员进行综合绩效考评，为项目绩效考评对项目团队成员进行绩效考评提供一种思维方式。