采用多种教学方法优化复变函数与积分变换教学

教学方法是实现复变函数与积分变换教育目的,完成教学任务的基本手段,它对于教学的成败、学生能力的发展都起着重大的作用。教学实践表明针对不同教学内容灵活采用多种教学方法,有利于提高教学质量,培养学生学习兴趣,帮助学生建立学好数学的信心,进而优化复变函数与积分变换的教学。     

扩展线性支出系统模型的改进

本文把扩展线性支出系统模型中的边际预算份额处理成随机变量,得出了相应的结果,并改进了原模型中的某些不足之处,使模型更接近实际,且应用起来比较方便。     

Nonparametric tests for the mean of a non-negative population

We construct level-α tests for testing the null hypothesis that the mean of a non-negative population falls below a prespecified nominal value. These tests make no assumption about the distribution function other than that it be supported on [0,∞). Simple tests are derived based on either the sample mean or the sample product. The nonparametric likelihood ratio test is also discussed in this context. We also derive the uniformly most powerful monotone (UMP) tests for special cases.     

大动态、抗饱和核爆炸光电变换器的研究

研究了大气层里核爆炸当量测量中光电转换技术，提出了电阻—二极管并联、多级串联的非线性负载，解决了大动态、抗饱和核爆炸光辐射光电变换器的负载设计，具有抗谷点饱和、抗阳光背景噪声干扰的特点，经实验现场考核，完全符合测量要求。     

Linux多进程运行的数据共享

介绍Linux多进程运行时的数据共享问题,包括对进程的数据段、堆栈段和代码段的共享,并简 要介绍Linux进程与WIN32进程在数据共享上的差异。     

Grouped penalization estimation of the osteoporosis data in the traditional Chinese medicine

Both continuous and categorical covariates are common in traditional Chinese medicine (TCM) research, especially in the clinical syndrome identification and in the risk prediction research. For groups of dummy variables which are generated by the same categorical covariate, it is important to penalize them group-wise rather than individually. In this paper, we discuss the group lasso method for a risk prediction analysis in TCM osteoporosis research. It is the first time to apply such a group-wise variable selection method in this field. It may lead to new insights of using the grouped penalization method to select appropriate covariates in the TCM research. The introduced methodology can select categorical and continuous variables, and estimate their parameters simultaneously. In our application of the osteoporosis data, four covariates (including both categorical and continuous covariates) are selected out of 52 covariates. The accuracy of the prediction model is excellent. Compared with the prediction model with different covariates, the group lasso risk prediction model can significantly decrease the error rate and help TCM doctors to identify patients with a high risk of osteoporosis in clinical practice. Simulation results show that the application of the group lasso method is reasonable for the categorical covariates selection model in this TCM osteoporosis research.     

Comparison of Efficiency of Stratified and Unequal Probability Sampling

In this article, a choice of the optimum sampling design to study a finite population is studied. Three sampling schemes are compared, viz., Sunter's procedure of unequal probability sampling, stratified sampling under optimum stratification, and simple random sampling without replacement. The comparison is made against a background of various correlation between stratification and survey variables and various variability in the variables. Under weak correlation and large variability, stratification appeared to be more efficient than Sunter's procedure. Under strong correlation and/or low variability in the variables, the latter procedure was the most efficient. Simple random sampling was usually the least efficient.     

Simple Tests for Exogeneity of a Binary Explanatory Variable in Count Data Regression Models

This article investigates power and size of some tests for exogeneity of a binary explanatory variable in count models by conducting extensive Monte Carlo simulations. The tests under consideration are Hausman contrast tests as well as univariate Wald tests, including a new test of notably easy implementation. Performance of the tests is explored under misspecification of the underlying model and under different conditions regarding the instruments. The results indicate that often the tests that are simpler to estimate outperform tests that are more demanding. This is especially the case for the new test.     

A ratio estimator of the total of a subpopulation

If the total of an auxiliary variable is known for an entire population but is unknown for some subpopulation, the usual estimator of the total of the primary variable for the subpopulation is the ratio estimator that uses the auxiliary total for the entire population. This article proposes a ratio estimator that uses an estimator of the auxiliary total over the subpopulation as suggested by Kish (1967, p. 438). Under some conditions, it is shown that the latter estimator is unbiased and has smaller variance than the former estimator in large simple random samples.