论弗雷格的残篇《论逻辑的普遍性》

弗雷格在其残篇《论逻辑的普遍性》中探讨了思想的普遍性问题,他引入了带有单个字母的辅助语言作为研究普遍性思想的桥梁,普遍性思想的结构分析及其真值条件是弗雷格未完成的内容。普遍性思想的构成部分都是不满足的;普遍性思想的真值条件可以通过理解不同层次概念之间的关系而直接把握,一阶量化理论是理解弗雷格残篇的关键。     

高校成本的性态分析与管理

高校成本按其与在校学生人数的相互关系可以分为固定成本和变动成本 ,对固定成本应实行定额管理 ,对变动成本应实行定率管理 .努力降低高校成本是高等教育产业化的客观要求 ,对扩大办学规模、加强竞争力、减轻学生经济负担等方面 ,都具有重要意义     

Random sequential packing in Rn

We introduce a Monte Carlo method for packing hypercubes in Rⁿ . Rigorous and conceptually simple, it is currently practical for n≥4. Experimental results indicate that Palasti's conjecture is false for R ² and K³ and still undecided for K⁴     

Variable selection for semiparametric errors-in-variables regression model with longitudinal data

In this paper, we focus on the variable selection for the semiparametric regression model with longitudinal data when some covariates are measured with errors. A new bias-corrected variable selection procedure is proposed based on the combination of the quadratic inference functions and shrinkage estimations. With appropriate selection of the tuning parameters, we establish the consistency and asymptotic normality of the resulting estimators. Extensive Monte Carlo simulation studies are conducted to examine the finite sample performance of the proposed variable selection procedure. We further illustrate the proposed procedure with an application.     

Fence methods for backcross experiments

Model search strategies play an important role in finding simultaneous susceptibility genes that are associated with a trait. More particularly, model selection via the information criteria, such as the BIC with modifications, have received considerable attention in quantitative trait loci mapping. However, such modifications often depend upon several factors, such as sample size, prior distribution, and the type of experiment, for example, backcross, intercross. These changes make it difficult to generalize the methods to all cases. The fence method avoids such limitations with a unified approach, and hence can be used more broadly. In this article, this method is studied in the case of backcross experiments throughout a series of simulation studies. The results are compared with those of the modified BIC method as well as some of the most popular shrinkage methods for model selection.     

Preliminary test estimators in double sampling with two auxiliary variables

An attempt has been mads to suggest some estimators for population mean in double sampling with two auxiliary variables., alternative to the usual regression estimator. When the experimenter has partial Information about the mean of the auxiliary variable or variables, preliminary test estimators can be used. The bias, mean square error, relative efficiency and optimum allocation of sample sizes are obtained for the suggested estimators.     

Biostatistical genetics and genetic epidemiology

It is well-known that when ranked set sampling (RSS) scheme is employed to estimate the mean of a population, it is more efficient than simple random sampling (SRS) with the same sample size. One can use a RSS analog of SRS regression estimator to estimate the population mean of Y using its concomitant variable X when they are linearly related. Unfortunately, the variance of this estimate cannot be evaluated unless the distribution of X is known. We investigate the use of resampling methods to establish confidence intervals for the regression estimation of the population mean. Simulation studies show that the proposed methods perform well in a variety of situations when the assumption of linearity holds, and decently well under mild non-linearity.     

Forward and backward stepping in variable selection

For stepwise regression and discriminant analysis the parameters F _in and F _out govern the inclusion and deletion of variables. The candidate variable with the biggest F—ratio is included if this exceeds F _inthe included variable with the smallest F—ratio is deleted if this is less than F _in If F _in ≧F _out; then return to a previous subset size implies improvement in the criterion measure. This result also holds for a generalization, stepwise multivariate analysis, which includes stepwise regression and discriminant analysis as special cases

Eliminations do not occur if forward regression and backward elimination yield the same sequence of subsets. Conversely, there is a more liberal stepping rule which always eliminates if the two sequences differ.     

Iterative regularisation in nonparametric instrumental regression

This paper considers the nonparametric regression model with an additive error that is correlated with the explanatory variables. Motivated by empirical studies in epidemiology and economics, it also supposes that valid instrumental variables are observed. However, the estimation of a nonparametric regression function by instrumental variables is an ill-posed linear inverse problem with an unknown but estimable operator. We provide a new estimator of the regression function that is based on projection onto finite dimensional spaces and that includes an iterative regularisation method (the Landweber–Fridman method). The optimal number of iterations and the convergence of the mean square error of the resulting estimator are derived under both strong and weak source conditions. A Monte Carlo exercise shows the impact of some parameters on the estimator and concludes on the reasonable finite sample performance of the new estimator.     

为学生布置合理的学习任务漫谈

本文重点探讨怎样才能最有效地运用学习任务去促进学习问题 ,即探讨影响学生学习活动的几个关键变量 ,介绍布置学习任务的基本原则