《双孝记》本事考

明传奇<双孝记>一直没有流传之本,且无佚文,因此仅有几种曲学书录著录,本事不详.今在整理<曲谱大成>稿本时,检出5枝<双孝记>佚曲,对照元传奇<王祥卧冰>,可知<双孝记>为<王祥卧冰>的改本,本事即为<二十四孝>的王祥故事.     

经济冷战研究综述

经济冷战是冷战的重要组成部分.西方学术界,特别是20世纪八、九十年代新档案解密以来,在宏观及微观研究中都取得了丰盛的成果,本文试图从其起源问题及其他问题两方面来分析.中国学术界在这一领域成果相对有限,主要是对经济冷战宏观性地描述及对具体事件的勾画.     

RECENTERED AND RESCALED INSTRUMENTAL VARIABLE ESTIMATION OF TOBIT AND PROBIT MODELS WITH ERRORS IN VARIABLES

Since Durbin (1954) and Sargan (1958), instrumental variable (IV) method has long been one of the most popular procedures among economists and other social scientists to handle linear models with errors-in-variables. A direct application of this method to nonlinear errors-in-variables models, however, fails to yield consistent estimators.

This article restricts attention to Tobit and Probit models and shows that simple recentering and rescaling of the observed dependent variable may restore consistency of the standard IV estimator if the true dependent variable and the IV's are jointly normally distributed. Although the required condition seems rarely to be satisfied by real data, our Monte Carlo experiment suggests that the proposed estimator may be quite robust to the possible deviation from normality.     

对高校学生公共体育兴趣的心理初探

本文从心理学的角度出发,对高校学生体育兴趣的产生、特点及变化原因作了初步探究,并提出了情感教学对体育兴趣形成的重要作用。     

球墨铸铁曲轴的气体软氮化

柴油机的珠墨铸铁曲轴系重要零件,气体软氮化应用到球墨铸铁领域还是一种新工艺,而且比中频淬火效果好,提高了曲轴的抗疲劳和抗磨损的能力。本文对气体软氮化的基本原理、设备、工艺参数的选择和催渗方法进行了探讨,对软氮化后曲轴的尺寸形状误差、光洁度和机械性能的提高等进行了较详细的试验、分析和比较,结果,经气体软氮化后的曲轴不但尺寸形站误差符合图纸要求外,而且抛去轻微氧化层后光洁度还略有提高,曲轴安全系数由1.3～1.6提高到1.70～1.77,并且一般不再发生疲劳断(?),使用寿命比原来寿命增加3.32倍以上。     

离心式叶轮机任意旋成面栅后区长度处理

文章揭示了叶轮栅后区长度L_3太长时,会使栅后区出气角β_2无法选择而造成无解的事实;表明在β_2变化的适当范围内,叶型后缘两侧速度差△Λ与β_2之间呈线性关系,而且该线性变化范围随L_3的增长而缩小;获得了随L_3作线性变化的规律。文章最后提出了离心式叶轮机任意旋成面叶栅流场的简单求解方法。     

Nonparametric density deconvolution by weighted kernel estimators

Nonparametric density estimation in the presence of measurement error is considered. The usual kernel deconvolution estimator seeks to account for the contamination in the data by employing a modified kernel. In this paper a new approach based on a weighted kernel density estimator is proposed. Theoretical motivation is provided by the existence of a weight vector that perfectly counteracts the bias in density estimation without generating an excessive increase in variance. In practice a data driven method of weight selection is required. Our strategy is to minimize the discrepancy between a standard kernel estimate from the contaminated data on the one hand, and the convolution of the weighted deconvolution estimate with the measurement error density on the other hand. We consider a direct implementation of this approach, in which the weights are optimized subject to sum and non-negativity constraints, and a regularized version in which the objective function includes a ridge-type penalty. Numerical tests suggest that the weighted kernel estimation can lead to tangible improvements in performance over the usual kernel deconvolution estimator. Furthermore, weighted kernel estimates are free from the problem of negative estimation in the tails that can occur when using modified kernels. The weighted kernel approach generalizes to the case of multivariate deconvolution density estimation in a very straightforward manner.     

Darling–Erdős limit results for change-point detection in panel data

We wish to test the null hypothesis if the means of N panels remain the same during the observation period of length T. A quasi-likelihood argument leads to self-normalized statistics whose limit distribution under the null hypothesis is double exponential. The main results are derived assuming that the each panel is based on independent observations and then extended to linear processes. The proofs are based on an approximation of the sum of squared CUSUM processes using the Skorokhod embedding scheme. A simulation study illustrates that our results can be used in case of small and moderate N and T. We apply our results to detect change in the “corruption index”.     

Two Pitfalls of Using Standard Regression Diagnostics When Both X and Y Have Measurement Error

Modern exploratory data analysis produces models that are not based on physical theory but that are consistent with pictures of the data. When both X and Y have error this can be risky, because important features are hidden. Two examples are given that show that systematic model departures and heteroscedasticity may not be detectable with standard regression diagnostics.     

Interpreting an Inequality in Multiple Regression

This article provides a method of interpreting a surprising inequality in multiple linear regression: the squared multiple correlation can be greater than the sum of the simple squared correlations between the response variable and each of the predictor variables. The interpretation is obtained via principal component analysis by studying the influence of some components with small variance on the response variable. One example is used as an illustration and some conclusions are derived.