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.     

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.     

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。     

An optimization approach to business buyer choice sets: How many suppliers should be included?

In many new or repeat purchasing situations, business buyers must decide how many suppliers to consider (a “choice set”) in determining which supplier(s) to actually buy from or contract with. This paper develops an optimization approach to determining the size of the choice set, taking into consideration buyer utility and search and evaluation costs. A theoretical model is developed for both one-time and repeat purchase situations. The model is estimated using empirical data received from bids received for procurement auctions. In these auctions, suppliers provide bids for steel pipe based on two product attributes (price and delivery time). Model sensitivity to small changes in parameters is also tested.     

Forecasting U.S. money growth using economic uncertainty measures and regularisation techniques

This paper examines the out-of-sample forecasting properties of six different economic uncertainty variables for the growth of the real M2 and real M4 Divisia money series for the U.S. using monthly data. The core contention is that information on economic uncertainty improves the forecasting accuracy. We estimate vector autoregressive models using the iterated rolling-window forecasting scheme, in combination with modern regularisation techniques from the field of machine learning. Applying the Hansen-Lunde-Nason model confidence set approach under two different loss functions reveals strong evidence that uncertainty variables that are related to financial markets, the state of the macroeconomy or economic policy provide additional informational content when forecasting monetary dynamics. The use of regularisation techniques improves the forecast accuracy substantially.