关注利益需求：民办高等职业院校教师队伍建设的着力点

随着社会的发展,民办高等职业教育作为高等教育的补充发挥着越来越重要的作用。然而民办高等职业院校由于各方面条件的欠缺,很难形成稳定的高素质的教师队伍。文章从利益的视角出发,分析教师在利益需求上的特点,从树立＂以人为本＂的工作理念,建立科学的政策制度,拓展行动导向等途径,建设民办高等职业院校教师队伍。     

构建新型农业经营主体与小农户利益联结机制

引导新型农业经营主体与小农户建立稳定有效的利益联结机制,对于促进乡村产业兴旺、农户增收致富至关重要。经过30多年发展,订单合同、专业合作等传统模式仍占主导地位,股份合作、农业产业化联合体等新型模式蓬勃兴起,多种联结方式并存的混合模式日获青睐,农村一二三产业呈深度融合发展态势。利益联结模式的选择,与农产品的行业特性、经营主体的发展阶段、农户的素质条件、农业生产条件以及双方的认识有很大关系。结合国外实践经验,当前要准确把握小农户的发展趋势,尊重新型农业经营主体和小农户的自愿选择,发挥各自优势,并加大政策支持,改善农业生产条件,稳定农村基本经营制度,发展适度规模经营,建立正向激励和失信名单机制,进一步优化指导服务,进一步密切新型农业经营主体和小农户的利益联结机制。     

提高农民种粮经济收益是确保国家粮食安全的关键

提高农民种粮经济收益是确保国家粮食安全的关键.粮食的自然、社会与经济特性决定了农民种粮经济收益偏低;较低的种粮经济收益驱使农民的主体行为选择不利于国家粮食安全;同时,发达国家粮食生产经验也表明确保国家粮食安全必须提高农民种粮经济收益,增强其种粮积极性.我国粮食总产量与粮食人均占有量均呈"N"形波动,呈现非稳定状态,表明我国粮食安全存在问题,其根本原因是我国种粮经济收益低且呈明显下降趋势.为缓解这一趋势,切实提高种粮经济效益,确保国家粮食安全,就需创建以控制种粮成本、降低种粮风险、提高粮食产品价值为核心的三维框架体系.     

The Benefits and Costs of Using Metadata to Improve Enterprise Document Search

The literature shows that there are many problems with enterprise document search. Studies reveal that typical knowledge workers spend between 10% and 20% of their time searching for documents they never find. While many argue that metadata can improve enterprise document search, in reality few organizations use metadata. This represents a missed opportunity. This article describes the results of two experiments that use simulation to evaluate the actual impact of metadata on the costs and benefits of enterprise search. The first study provides quantitative evidence of the increase in recall and precision that stems from the use of metadata‐enhanced document searches. The second study demonstrates that simple metadata structures can be nearly as effective as complex ones, implying that the cost of creating and maintaining metadata is likely to be lower than generally thought. This is the first study to provide explicit quantitative evidence of the gains that can be achieved from the use of metadata, and one of only a handful of studies that examines the cost of creating and maintaining metadata.     

Cost‐Benefit Analysis for Optimization of Risk Protection Under Budget Constraints

Cost‐benefit analysis (CBA) is commonly applied as a tool for deciding on risk protection. With CBA, one can identify risk mitigation strategies that lead to an optimal tradeoff between the costs of the mitigation measures and the achieved risk reduction. In practical applications of CBA, the strategies are typically evaluated through efficiency indicators such as the benefit‐cost ratio (BCR) and the marginal cost (MC) criterion. In many of these applications, the BCR is not consistently defined, which, as we demonstrate in this article, can lead to the identification of suboptimal solutions. This is of particular relevance when the overall budget for risk reduction measures is limited and an optimal allocation of resources among different subsystems is necessary. We show that this problem can be formulated as a hierarchical decision problem, where the general rules and decisions on the available budget are made at a central level (e.g., central government agency, top management), whereas the decisions on the specific measures are made at the subsystem level (e.g., local communities, company division). It is shown that the MC criterion provides optimal solutions in such hierarchical optimization. Since most practical applications only include a discrete set of possible risk protection measures, the MC criterion is extended to this situation. The findings are illustrated through a hypothetical numerical example. This study was prepared as part of our work on the optimal management of natural hazard risks, but its conclusions also apply to other fields of risk management.     

Fixed Effects and Bias Due to the Incidental Parameters Problem in the Tobit Model

The maximum likelihood estimator (MLE) in nonlinear panel data models with fixed effects is widely understood (with a few exceptions) to be biased and inconsistent when T, the length of the panel, is small and fixed. However, there is surprisingly little theoretical or empirical evidence on the behavior of the estimator on which to base this conclusion. The received studies have focused almost exclusively on coefficient estimation in two binary choice models, the probit and logit models. In this note, we use Monte Carlo methods to examine the behavior of the MLE of the fixed effects tobit model. We find that the estimator's behavior is quite unlike that of the estimators of the binary choice models. Among our findings are that the location coefficients in the tobit model, unlike those in the probit and logit models, are unaffected by the “incidental parameters problem.” But, a surprising result related to the disturbance variance emerges instead - the finite sample bias appears here rather than in the slopes. This has implications for estimation of marginal effects and asymptotic standard errors, which are also examined in this paper. The effects are also examined for the probit and truncated regression models, extending the range of received results in the first of these beyond the widely cited biases in the coefficient estimators.     

Estimation of bivariate and marginal distributions with censored data

Summary. Consider a pair of random variables, both subject to random right censoring. New estimators for the bivariate and marginal distributions of these variables are proposed. The estimators of the marginal distributions are not the marginals of the corresponding estimator of the bivariate distribution. Both estimators require estimation of the conditional distribution when the conditioning variable is subject to censoring. Such a method of estimation is proposed. The weak convergence of the estimators proposed is obtained. A small simulation study suggests that the estimators of the marginal and bivariate distributions perform well relatively to respectively the Kaplan–Meier estimator for the marginal distribution and the estimators of Pruitt and van der Laan for the bivariate distribution. The use of the estimators in practice is illustrated by the analysis of a data set.     

A method for modifying multiple testing procedures

Many recent multiple testing papers have provided more efficient and/or robust methodology for control of a particular error rate. However, different multiple testing scenarios call for the control of different error rates. Hence, the procedure possessing the desired optimality and/or robustness properties may not be applicable to the problem at hand. This paper provides a general method for extending any multiple testing procedure to control any error rate, thereby allowing for the procedure possessing the desired properties to be used to control the most relevant error rate. As an example, two popular procedures that were originally designed to control the marginal and positive False Discovery Rate are extended to control the False Discovery Rate and Family-wise Error Rate. It is shown that optimality and/or robustness properties of the original procedure are retained when it is modified using the proposed method.     

General partially linear varying-coefficient transformation model with right censored data

In this paper, a unified maximum marginal likelihood estimation procedure is proposed for the analysis of right censored data using general partially linear varying-coefficient transformation models (GPLVCTM), which are flexible enough to include many survival models as its special cases. Unknown functional coefficients in the models are approximated by cubic B-spline polynomial. We estimate B-spline coefficients and regression parameters by maximizing marginal likelihood function. One advantage of this procedure is that it is free of both baseline and censoring distribution. Through simulation studies and a real data application (VA data from the Veteran's Administration Lung Cancer Study Clinical Trial), we illustrate that the proposed estimation procedure is accurate, stable and practical.     

Marginal Nested Interactions for Contingency Tables

We introduce a new definition of generalized marginal interactions, called marginal nested interactions, which includes baseline, local, continuation and reverse continuation logits and odds ratios as special cases. The significant aspect of this definition is the inclusion of new types of logits and odds ratios that can handle non-ordinal, ordinal and partially ordered categorical variables in a flexible and appropriate way. It is proved also that the marginal nested interactions define a saturated model of a multi-way contingency table.