基于Excel的道路工程计算技术

围绕数据组织、数据的联动检索技术、Excel高级函数、Excel自定义函数、Excel VBA程序的开发过程,系统地建立了基于Excel的道路工程计算的框架模板。     

商务智能技术在组织机构代码中的应用研究

商务智能技术目前主要应用于商务领域,在组织机构代码中的应用还有待研究。基于Oracle的商务智能工具,介绍了在组织机构代码中应用商务智能技术的基本方法和过程。     

基于SQL server的物流企业数据仓库设计和应用

数据仓库,作为数据库极富潜力的应用领域,近年来已在中国引起了IT行业的广泛关注。有越来越多的企业希望能够通过切实可行的数据仓库应用,来提高企业的决策支持水平。文中提出了一个基于SQL server的物流企业数据仓库的设计和应用。     

Spatial wage disparities: Sorting matters!

Spatial wage disparities can result from spatial differences in the skill composition of the workforce, in non-human endowments, and in local interactions. To distinguish between these explanations, we estimate a model of wage determination across local labour markets using a very large panel of French workers. We control for worker characteristics, worker fixed effects, industry fixed effects, and the characteristics of the local labour market. Our findings suggest that individual skills account for a large fraction of existing spatial wage disparities with strong evidence of spatial sorting by skills. Interaction effects are mostly driven by the local density of employment. Not controlling for worker heterogeneity leads to very biased estimates of interaction effects. Endowments only appear to play a small role.     

State dependence in youth labor market experiences, and the evaluation of policy interventions

We investigate the extent and type of state dependence in labor market outcomes for young low-skilled Australians. Our model allows for three labor force states, employment, unemployment and out of the labor force, and for observed and unobserved heterogeneity. We find evidence of occurrence dependence, but no lagged duration dependence. A past employment spell increases the probability of employment in the future, but the length of the spell does not matter. A past spell of unemployment undoes the positive benefits from a spell in employment. Interpretations of these effects and implications for labor market policies are discussed.     

Maximum likelihood estimation of a binomial proportion using one‐sample misclassified binary data

In this article, we construct two likelihood‐based confidence intervals (CIs) for a binomial proportion parameter using a double‐sampling scheme with misclassified binary data. We utilize an easy‐to‐implement closed‐form algorithm to obtain maximum likelihood estimators of the model parameters by maximizing the full‐likelihood function. The two CIs are a naïve Wald interval and a modified Wald interval. Using simulations, we assess and compare the coverage probabilities and average widths of our two CIs. Finally, we conclude that the modified Wald interval, unlike the naïve Wald interval, produces close‐to‐nominal CIs under various simulations and, thus, is preferred in practice. Utilizing the expressions derived, we also illustrate our two CIs for a binomial proportion parameter using real‐data example.     

Selecting Sites for New Facilities Using Data Envelopment Analysis

This paper develops a mathematical programming model for obtaining a best set of sites for planned facilities. The model is concerned with those situations where resource constraints are present. The specific setting for the paper involves the selection of sites for a set of retail outlets, wherein the ratio of aggregate outputs to inputs for the selected set is maximal among all possible sets that could be chosen. At the same time, the model guarantees that the only sets of stores allowable are those for which the available resources are used to the maximum extent possible.     

云数据中心网络虚拟化技术的实现路径

当今社会,随着科学技术的迅猛发展,信息技术实现了持续快速发展。在大环境的影响下,云计算与网络虚拟化技术应运而生。该技术的产生和发展促使云数据中心网络的变革与发展成为新一轮技术革命的核心内容之一。当前,IT行业对数据中心网络的需求越来越大。基于此,论文论述了云数据中心网络虚拟化技术的实现路径。     

Selection of influential variables in ordinal data with preponderance of zeros

Presence of excess zero in ordinal data is pervasive in areas like medical and social sciences. Unfortunately, analysis of such kind of data has so far hardly been looked into, perhaps for the reason that the underlying model that fits such data, is not a generalized linear model. Obviously some methodological developments and intensive computations are required. The current investigation is concerned with the selection of variables in such models. In many occasions where the number of predictors is quite large and some of them are not useful, the maximum likelihood approach is not the automatic choice. As, apart from the messy calculations involved, this approach fails to provide efficient estimates of the underlying parameters. The proposed penalized approach includes ?₁ penalty (LASSO) and the mixture of ?₁ and ?₂ penalties (elastic net). We propose a coordinate descent algorithm to fit a wide class of ordinal regression models and select useful variables appearing in both the ordinal regression and the logistic regression based mixing component. A rigorous discussion on the selection of predictors has been made through a simulation study. The proposed method is illustrated by analyzing the severity of driver injury from Michigan upper peninsula road accidents.     

Comparison of dimension reduction methods for DEA under big data via Monte Carlo simulation

Data with large dimensions will bring various problems to the application of data envelopment analysis (DEA). In this study, we focus on a “big data” problem related to the considerably large dimensions of the input-output data. The four most widely used approaches to guide dimension reduction in DEA are compared via Monte Carlo simulation, including principal component analysis (PCA-DEA), which is based on the idea of aggregating input and output, efficiency contribution measurement (ECM), average efficiency measure (AEC), and regression-based detection (RB), which is based on the idea of variable selection. We compare the performance of these methods under different scenarios and a brand-new comparison benchmark for the simulation test. In addition, we discuss the effect of initial variable selection in RB for the first time. Based on the results, we offer guidelines that are more reliable on how to choose an appropriate method.