Lower Risk Bounds and Properties of Confidence Sets for Ill‐Posed Estimation Problems with Applications to Spectral Density and Persistence Estimation,Unit Roots,and Estimation of Long Memory Parameters

Important estimation problems in econometrics like estimating the value of a spectral density at frequency zero, which appears in the econometrics literature in the guises of heteroskedasticity and autocorrelation consistent variance estimation and long run variance estimation, are shown to be “ill‐posed” estimation problems. A prototypical result obtained in the paper is that the minimax risk for estimating the value of the spectral density at frequency zero is infinite regardless of sample size, and that confidence sets are close to being uninformative. In this result the maximum risk is over commonly used specifications for the set of feasible data generating processes. The consequences for inference on unit roots and cointegration are discussed. Similar results for persistence estimation and estimation of the long memory parameter are given. All these results are obtained as special cases of a more general theory developed for abstract estimation problems, which readily also allows for the treatment of other ill‐posed estimation problems such as, e.g., nonparametric regression or density estimation.     

Nonparametric Estimation with Nonlinear Budget Sets

Choice models with nonlinear budget sets provide a precise way of accounting for the nonlinear tax structures present in many applications. In this paper we propose a nonparametric approach to estimation of these models. The basic idea is to think of the choice, in our case hours of labor supply, as being a function of the entire budget set. Then we can do nonparametric regression where the variable in the regression is the budget set. We reduce the dimensionality of this problem by exploiting structure implied by utility maximization with piecewise linear convex budget sets. This structure leads to estimators where the number of segments can differ across observations and does not affect accuracy. We give consistency and asymptotic normality results for these estimators. The usefulness of the estimator is demonstrated in an empirical example, where we find it has a large impact on estimated effects of the Swedish tax reform.     

尺度参照语言测试的基本描述和题项分析

通过分数分布、题项分析及CRT题项分析说明语言测试中的CRT和NRT是一对具有本质不同的两种测试 ,从对区分熟练组与非熟练组考生和检测考生对掌握知识及教学目的是否达到教学大纲要求等方面 ,CRT更胜于NRT。文章同时提出目前对多数从事语言教学的教师来说还是比较陌生的切线指标分析方法 ,不但可以用于检测判别学生的成绩 ,还可以检测教师所教内容与学生确实已吸收知识之间的适度值。     

FP-array在计算机犯罪挖掘中的应用

针对现代社会计算机犯罪中电子证据的收集难度很大,且海量的电子证据之间的相关性不易分析的问题,对基于FP-Tree的最大频繁模式(FP-Max)挖掘算法的优缺点进行了分析,根据FP-Max算法所存在的缺点并结合实际提出一种通过构建FP矩阵的FP-array的高性能关联规则挖掘算法,并将该算法用于典型的计算机犯罪电子证据的相关性数据挖掘中,可用于成功地挖掘比较常见的五类计算机犯罪数据,挖掘结果可为实际的破案过程提供重要参考。     

五星现代农业科技示范园规划设计

结合五星现代农业科技示范园实际项目,对这种特殊性质的“园”进行了较为详细的总体规划设计方面的探讨。旨在阐述在现代农业科技示范园的规划中,如何立足于高新农业科技、旅游观光、产业经营为一体的经营管理模式,全面展示现代农业发展的新模式,为传统农业的改革提供了创新之路。     

Survey of research on fuzzy sets in the Soviet Union

This paper surveys Soviet literature of fuzzy set theory and applications. A bibliography containing about 250 titles is presented.     

C3 managerial decision aids for dealing with uncer tainty - various approaches

In Part I the context of military planning is examined from the standpoint of uncertainty. There is a special focus on the uncertainty surrounding the subject of C3 (command, control and communications). It is argued that military planning is a fuzzy process. Tools being developed within the OJCS to cope with the subject of tactical C3 are introduced. In Part II case histories of two decision aids, which deal directly with uncertainty from two distinct vantage points, are presented.     

Vague集间相似度量公式的一个分辨力检验法

在应用中相似度量公式的选择是应用成功的关键,提出一种Vague集间相似度量的检验法,可应用于检验一个Vague集间的相似度量公式的分辨能力。     

网络信息过滤的方法和技术研究

网络信息过滤是根据一定的标准和利用一定的工具从动态的网络信息流中选取相关的信息或剔除不相关信息的一系列过程.论述了网络信息过滤的原理,从用户的信息需求与表示,文本的表示技术等方面探讨了网络信息过滤的方法和技术以及存在的问题,提出Vague集之间的相似度量在网络信息过滤中的应用。     

Choosing colleges: Identifying and modeling choice sets

Although largely ignored in the growing literature about college choice that adopts a random utility modeling framework, there is reason to believe that the estimates are sensitive to the criteria used to define the choice sets. Accordingly, this paper examines the methodological and substantive implications of using merit (class rank) and socioeconomic (high school strata) to define college choice sets. Using Texas as a case study, results show that criteria used to constrain choice sets—and type of high school attend in particular—not only produce quite different post-secondary institutional profiles, but also different estimates of institutional attributes on students’ top choice. These findings have methodological implications for future research about college choice and substantive importance for the prospects that the Texas top 10% law, which guarantees automatic admission to students who graduate in the top decile of their high school class, will equalize access to the state’s competitive public institutions.