新时期高校图书馆期刊管理的思考

随着信息网络技术的发展 ,高校图书馆期刊管理将发生深刻变化。本文对高校图书馆期刊管理的现状 ,期刊的发展趋势 ,对高校图书馆期刊的现代化管理和利用提出几点看法。     

英语精读课中影响学生听课效率的因素及对策

精读课是高校英语教学中最重要的课程。但是 ,在教学中 ,由于受各方面因素的影响 ,学生的听课效率并不理想。笔者通过问卷调查的方式 ,分析了影响学生听课效率的原因 ,并在此基础上提出相应的对策 ,认为要提高学生的听课效率 ,必须提高学生对精读课学习的认识、选好教材、改变教学方法 ,才能实现教学目的。     

How Plant Managers' Experiences and Attitudes Toward Sustainability Relate to Operational Performance

Managers are increasingly faced with pressure to think not just about profits, but also about their organization's environmental and social performance. This research provides a first examination of operational managers' experiences with and attitudes about employee well‐being and environmental issues, how these factors impact employee well‐being and environmental performance, and how the three performance measures interrelate. We use violations of Occupational Safety and Health Administration regulations and Toxic Release Inventory reports of emissions as proxies for employee well‐being and environmental performance. Our findings suggest that operational managers do not (yet) think in sustainability terms. However, employee well‐being and environmental performance do interact in a significant way with operational performance. Hence, operational managers would benefit from a more complete understanding of the relationships among the elements of the triple bottom line.     

国外技术性贸易措施对我国农产品出口的影响——以我国苹果及大蒜出口为例

技术性贸易措施正逐步取代关税和配额,成为我国加入WTO后农产品出口面临的主要障碍。以我国苹果及大蒜出口为例,以国外农药最大残留限量标准作为技术性贸易措施的量化指标,利用HMR两阶段模型研究国外技术性贸易措施对我国农产品出口的影响。研究表明：国外农药最大残留限量标准对我国苹果及大蒜的出口概率有显著的抑制作用,对我国苹果的出口贸易量也有显著的抑制作用。具体而言,与发达国家相比,发展中国家的农药最大残留限量标准更为显著地抑制了我国苹果及大蒜的出口概率;与发展中国家相比,发达国家的农药最大残留限量标准则更为显著地抑制了我国苹果及大蒜的出口贸易量。     

Spatial Cluster Detection for Longitudinal Outcomes Using Administrative Regions

This article proposes a new spatial cluster detection method for longitudinal outcomes that detects neighborhoods and regions with elevated rates of disease while controlling for individual level confounders. The proposed method, CumResPerm, utilizes cumulative geographic residuals through a permutation test to detect potential clusters which are defined as sets of administrative regions, such as a town or group of administrative regions. Previous cluster detection methods are not able to incorporate individual level data including covariate adjustment, while still being able to define potential clusters using informative neighborhood or town boundaries. Often, it is of interest to detect such spatial clusters because individuals residing in a town may have similar environmental exposures or socioeconomic backgrounds due to administrative reasons, such as zoning laws. Therefore, these boundaries can be very informative and more relevant than arbitrary clusters such as the standard circle or square. Application of the CumResPerm method will be illustrated by the Home Allergens and Asthma prospective cohort study analyzing the relationship between area or neighborhood residence and repeated measured outcome, occurrence of wheeze in the last six months, while taking into account mobile locations.     

On Blest’s Measure of Kurtosis Adjusted for Skewness

We reconsider the derivation of Blest’s (2003) skewness adjusted version of the classical moment-based coefficient of kurtosis and propose an adaptation of it which generally eliminates the effects of asymmetry a little more successfully. Lower bounds are provided for the two skewness adjusted kurtosis moment measures as functions of the classical coefficient of skewness. The results from a Monte Carlo experiment designed to investigate the sampling properties of numerous moment-based estimators of the two skewness adjusted kurtosis measures are used to identify those estimators with lowest mean squared error for small to medium sized samples drawn from distributions with varying levels of asymmetry and tailweight.     

Depth Measures for Multivariate Functional Data

In this article, we address the problem of mining and analyzing multivariate functional data. That is, data where each observation is a set of possibly correlated functions. Complex data of this kind is more and more common in many research fields, particularly in the biomedical context. In this work, we propose and apply a new concept of depth measure for multivariate functional data. With this new depth measure it is possible to generalize robust statistics, such as the median, to the multivariate functional framework, which in turn allows the application of outlier detection, boxplots construction, and nonparametric tests also in this more general framework. We present an application to Electrocardiographic (ECG) signals.     

Measures of uniformity in experimental designs: A selective overview

ABSTRACT

Discrepancies are measures which are defined as the deviation between the empirical and the theoretical uniform distribution. In this way, discrepancy is a measure of uniformity which provides a way of construction a special kind of space filling designs, namely uniform designs. Several discrepancies have been proposed in recent literature. A brief, selective review of these measures including some construction algorithms are given in this paper. Furthermore, a critical discussion along with some comparisons is provided, as well.     

A note on stratification and gain in precision in estimating diversity from large samples

Several indices of entropy have been suggested in the literature as weighted diversity measures of a population with respect to a classification process. Among them, Shannon's entropy and Havrda -Charvát's non-additive entropies of order a, have been exhaustively used.

When the population is finite but too large to be censused, the diversity with respect to a given classification process must be estimated from a sample.

In this note, on the basis of an asymptotic study of the sample indices in the stratified random sampling, we are going to confirm that when we deal with large samples one can guarantee a gain in precision from stratified random over simple random sampling. This gain becomes considerable when the ‘inaccuracy" (as intended by Kerridge and Rathie and Kannapan) between the frequency vector in each stratum and that in the whole population, varies greatly from stratum to stratum.     

Wild Bootstrap of the Sample Mean in the Infinite Variance Case

It is well known that the standard independent, identically distributed (iid) bootstrap of the mean is inconsistent in a location model with infinite variance (α-stable) innovations. This occurs because the bootstrap distribution of a normalised sum of infinite variance random variables tends to a random distribution. Consistent bootstrap algorithms based on subsampling methods have been proposed but have the drawback that they deliver much wider confidence sets than those generated by the iid bootstrap owing to the fact that they eliminate the dependence of the bootstrap distribution on the sample extremes. In this paper we propose sufficient conditions that allow a simple modification of the bootstrap (Wu, 1986 Wu , C. F. J. ( 1986 ). Jackknife, bootstrap, and other resampling methods . Annals of Statistics 14 : 1261 – 1295 .[Crossref], [Web of Science ®] , [Google Scholar]) to be consistent (in a conditional sense) yet to also reproduce the narrower confidence sets of the iid bootstrap. Numerical results demonstrate that our proposed bootstrap method works very well in practice delivering coverage rates very close to the nominal level and significantly narrower confidence sets than other consistent methods.