学籍管理的电子信息化

对电子信息化的学籍档案管理工作模式进行了初步探讨。从学籍信息获取、维护、查询以及交换方面进行了详细描述,并结合典型校园网信息平台的特点,给出了关键部分的实现方式,提出了实现以上方案所需的关键条件。最后对学籍管理电子信息的前景做了展望。     

高职生心理问题及教育对策浅议

提高高职生心理健康水平,不仅是高职生成人之需要,更是高职院校教育任务之必要。本文分析了高职学生的心理问题及主要表现,对其产生原因进行了探讨,并结合实际,就心理问题的对策提出了一些思考。     

小麦籽粒产量选择指数的研究

本文用35个普通小麦品种(系)为试验材料,研究单株籽粒产量的遗传参数及选择指数。结果表明:单株籽粒产量的遗传力、遗传进度均较低,直接选择效果较差。通过各独立性状进行间接选择,效果亦不理想。将各性状分别与单株产量建立指数方程进行综合选择,则以穗粒数、剑叶面积、单株穗数为好,相对效率分别为112.47%、107.92%、103.20%。在所有三个以上性状所组成的选择指数方程中,凡包含了单株产量的构成因素:穗粒数、千粒重、单株穗数的指数方程,其遗传进度最大,相对效率最高。由穗粒数、千粒重、单株穗数、单株生物产量、剑叶面积所组成的选择指数方程:y=0.2127x_1+0.1932x_2+3.8454x-3-0.3406x_4+0.0598x_7,相对效率达173.13%,超过了大部分由6个以上性状所组成的选择指数方程的遗传进度。在育种工作中,对这些性状综合筛选,将事半功倍。     

Local model uncertainty and incomplete-data bias (with discussion)

Summary.  Problems of the analysis of data with incomplete observations are all too familiar in statistics. They are doubly difficult if we are also uncertain about the choice of model. We propose a general formulation for the discussion of such problems and develop approximations to the resulting bias of maximum likelihood estimates on the assumption that model departures are small. Loss of efficiency in parameter estimation due to incompleteness in the data has a dual interpretation: the increase in variance when an assumed model is correct; the bias in estimation when the model is incorrect. Examples include non-ignorable missing data, hidden confounders in observational studies and publication bias in meta-analysis. Doubling variances before calculating confidence intervals or test statistics is suggested as a crude way of addressing the possibility of undetectably small departures from the model. The problem of assessing the risk of lung cancer from passive smoking is used as a motivating example.     

《花庵词选》版本源流考

黄羿所编撰的《花庵词选》是宋代极为重要的一部词选，分为《唐宋诸贤绝妙词选》和《中兴以来绝妙词选》两部分，历来刊本较为复杂，或一选单行，或两选合刻。文章从单行本和合刻本入手，梳理和分析了《花庵词选》的历代刊本，介绍了各种刊本的相关内容及流传关系，为深入研究《花庵词选》的版本目录奠定了基础。     

Minimax subset selection for the multinomial distribution

Let (X₁,…,X_k) be a multinomial vector with unknown cell probabilities (p₁,?,p_k). A subset of the cells is to be selected in a way so that the cell associated with the smallest cell probability is included in the selected subset with a preassigned probability, P¹. Suppose the loss is measured by the size of the selected subset, S. Using linear programming techniques, selection rules can be constructed which are minimax with respect to S in the class of rules which satisfy the P¹-condition. In some situations, the rule constructed by this method is the rule proposed by Nagel (1970). Similar techniques also work for selection in terms of the largest cell probability.     

Is the Selected Population the Best?—Location and Scale Parameter Cases

We observe X ₁,…,X _k , where X _i has density f(x,θ _i ) possessing monotone likelihood ratio. The best population corresponds to the largest θ _i . We select the population corresponding to the largest X _i . The goal is to attach the best possible p-value to the inference: the selected population has the uniquely largest θ _i . Gutmann and Maymin (1987 Gutmann , S. , Maymin , Z. ( 1987 ). Is the selected population the best? Ann. Statist . 15 : 456 – 461 .[Crossref], [Web of Science ®] , [Google Scholar]) considered the location parameter case and derived the supremum of the error probability by conditioning on S, the index of the largest X _i . Using this conditioning approach, Kannan and Panchapakesan (2009 Kannan , N. , Panchapakesan , S. ( 2009 ). Does the selected normal population have the smallest variance? Amer. J. Math. Management Sci . 29 : To appear . [Google Scholar]) considered the problem for the gamma family. We consider here a unified approach to both the location and scale parameter cases, and obtain the supremum of the error probability without using conditioning.     

Akaike Information Criterion for Selecting Variables in the Nested Error Regression Model

The Akaike Information Criterion (AIC) is developed for selecting the variables of the nested error regression model where an unobservable random effect is present. Using the idea of decomposing the likelihood into two parts of “within” and “between” analysis of variance, we derive the AIC when the number of groups is large and the ratio of the variances of the random effects and the random errors is an unknown parameter. The proposed AIC is compared, using simulation, with Mallows' C _p , Akaike's AIC, and Sugiura's exact AIC. Based on the rates of selecting the true model, it is shown that the proposed AIC performs better.     

A unified approach to multilevel sample selection models

Abstract

We propose a unified approach for multilevel sample selection models using a generalized result on skew distributions arising from selection. If the underlying distributional assumption is normal, then the resulting density for the outcome is the continuous component of the sample selection density and has links with the closed skew-normal distribution (CSN). The CSN distribution provides a framework which simplifies the derivation of the conditional expectation of the observed data. This generalizes the Heckman’s two-step method to a multilevel sample selection model. Finite-sample performance of the maximum likelihood estimator of this model is studied through a Monte Carlo simulation.     

Extended BIC for linear regression models with diverging number of relevant features and high or ultra-high feature spaces

In many conventional scientific investigations with high or ultra-high dimensional feature spaces, the relevant features, though sparse, are large in number compared with classical statistical problems, and the magnitude of their effects tapers off. It is reasonable to model the number of relevant features as a diverging sequence when sample size increases. In this paper, we investigate the properties of the extended Bayes information criterion (EBIC) (Chen and Chen, 2008) for feature selection in linear regression models with diverging number of relevant features in high or ultra-high dimensional feature spaces. The selection consistency of the EBIC in this situation is established. The application of EBIC to feature selection is considered in a SCAD cum EBIC procedure. Simulation studies are conducted to demonstrate the performance of the SCAD cum EBIC procedure in finite sample cases.