构建基于过程管理的《高等数学》课平时成绩管理模型

基于教学过程，构建学生平时成绩管理模型，在Excel中用VBA编程，使平时成绩真正做到平时得到、实时统计。     

改进模式对比研究

本文从企业竞争优势来源出发,研究企业动态能力的选择和实施问题。对比分析了三种企业普遍采用的改进模式:流程再造、阶段式改进方式以及持续改进模式。着重对容易引起混淆的持续改进和阶段式改进两种模式从指标、组织作用机制、竞争能力构建等方面进行了较为详尽的对比分析,并结合两种改进模式各自的特点,提出了流程化阶段式改进的观点,分析了改进模式的选择如何影响企业长期竞争能力。     

Realized Volatility: A Review

This article reviews the exciting and rapidly expanding literature on realized volatility. After presenting a general univariate framework for estimating realized volatilities, a simple discrete time model is presented in order to motivate the main results. A continuous time specification provides the theoretical foundation for the main results in this literature. Cases with and without microstructure noise are considered, and it is shown how microstructure noise can cause severe problems in terms of consistent estimation of the daily realized volatility. Independent and dependent noise processes are examined. The most important methods for providing consistent estimators are presented, and a critical exposition of different techniques is given. The finite sample properties are discussed in comparison with their asymptotic properties. A multivariate model is presented to discuss estimation of the realized covariances. Various issues relating to modelling and forecasting realized volatilities are considered. The main empirical findings using univariate and multivariate methods are summarized.     

Testing homogeneity against trend based on rank in one-way layout

In this paper, we are concerned with testing homogeneity against trend. Parsons (1979) considered the exact distribution of the test statistic based on the Wilcoxon type scores. We extend his result to the case of the general scores. Then we give a table of significance probabilities for the Fisher-Yates normal scores. We also study the asymptotic distribution of the test statis-tic based on the general scores under the null hypothesis, and the asymptotic relative efficiency against Bartholomew's likelihood ratio test assuming normality     

A statistical uncertainty principle for estimating the time of a discrete shift in the mean of a continuous time random process

The purpose of this article is to present a statistical uncertainty principle that can be used when localizing a single change in the mean of a band-limited stationary random process. The statistical model investigated is a continuous time process that experiences a shift in its mean. This continuous time process is presumed to be sampled using an ideal low-pass filter. The least squares estimate of the location of the change in mean is asymptotically Gaussian. The standard deviation of the least squares estimate of the location of the change-point provides a physical limit to the accuracy of the estimate of the time of the mean shift which cannot be bettered.     

Optimal Experimental Design Strategies for Detecting Hormesis

Hormesis is a widely observed phenomenon in many branches of life sciences, ranging from toxicology studies to agronomy, with obvious public health and risk assessment implications. We address optimal experimental design strategies for determining the presence of hormesis in a controlled environment using the recently proposed Hunt‐Bowman model. We propose alternative models that have an implicit hormetic threshold, discuss their advantages over current models, and construct and study properties of optimal designs for (i) estimating model parameters, (ii) estimating the threshold dose, and (iii) testing for the presence of hormesis. We also determine maximin optimal designs that maximize the minimum of the design efficiencies when we have multiple design criteria or there is model uncertainty where we have a few plausible models of interest. We apply these optimal design strategies to a teratology study and show that the proposed designs outperform the implemented design by a wide margin for many situations.     

Blur-generated non-separable space–time models

Statistical space–time modelling has traditionally been concerned with separable covariance functions, meaning that the covariance function is a product of a purely temporal function and a purely spatial function. We draw attention to a physical dispersion model which could model phenomena such as the spread of an air pollutant. We show that this model has a non-separable covariance function. The model is well suited to a wide range of realistic problems which will be poorly fitted by separable models. The model operates successively in time: the spatial field at time t +1 is obtained by 'blurring' the field at time t and adding a spatial random field. The model is first introduced at discrete time steps, and the limit is taken as the length of the time steps goes to 0. This gives a consistent continuous model with parameters that are interpretable in continuous space and independent of sampling intervals. Under certain conditions the blurring must be a Gaussian smoothing kernel. We also show that the model is generated by a stochastic differential equation which has been studied by several researchers previously.     

Scholastic ability vs family background in educational success: evidence from Danish sample survey data

This research examines the role of scholastic ability and family background variables in the determination of educational attainment in Denmark. A categorical representation of the highest level of education attained by the individual is the dependent variable. It is analyzed by procedures that take account of the presence of unobservable factors. Parent’s education and occupation, along with an indicator of scholastic ability which is represented by a set of aptitude tests, explain a small but significant portion of the variation in their children’s educational success. Women are shown to respond differently to their environments than men, and including these test scores does not remove the need to deal with unmeasured attributes. On the basis of the available data, family background variables as a group contribute more to the explained variation in the data than the test scores. Finally, credit constraints do not appear to be a factor in educational attainments.

When and Why are Principal Component Scores a Good Tool for Visualizing High‐dimensional Data?

Principal component analysis is a popular dimension reduction technique often used to visualize high‐dimensional data structures. In genomics, this can involve millions of variables, but only tens to hundreds of observations. Theoretically, such extreme high dimensionality will cause biased or inconsistent eigenvector estimates, but in practice, the principal component scores are used for visualization with great success. In this paper, we explore when and why the classical principal component scores can be used to visualize structures in high‐dimensional data, even when there are few observations compared with the number of variables. Our argument is twofold: First, we argue that eigenvectors related to pervasive signals will have eigenvalues scaling linearly with the number of variables. Second, we prove that for linearly increasing eigenvalues, the sample component scores will be scaled and rotated versions of the population scores, asymptotically. Thus, the visual information of the sample scores will be unchanged, even though the sample eigenvectors are biased. In the case of pervasive signals, the principal component scores can be used to visualize the population structures, even in extreme high‐dimensional situations.     

孔尚任宫词名称考辨

由于研究资料缺乏,学界对孔尚任宫词的名称及其相互关系意见不一。孔尚任宫词有"孔东塘宫词"、"宫词百首"、"续古宫词"、"拟古宫词"等不同名称,有的学者认为"宫词百首"也就是孔尚任早年所作《绝句百首》。考察南开大学图书馆所藏介安堂刻本《续古宫词》,可知孔尚任宫词正式名称为《续古宫词》,又名《拟古宫词》、《孔东塘宫词》,与《绝句百首》不是相同的作品。