用多种统计分析方法检验评价学生成绩与考试制度

余力．用多种统计分析方法检验评价学生成绩与考试制度．本文综合运用多元ＨｏｔｅｌｉｎｇＴ２检验方法和一元ｔ检验方法,对某校两组学生的学习成绩进行对比分析、评价。实例分析表明,“取消补考,完善重修制度”这一考试制度的改革方案是合理的,行之有效的,同时也是切实可行的。本文也初步尝试了处理病态数据的方法     

研究生入学成绩对后续课程影响的统计研究

<正>一、引 言 随着教育体制改革的不断深入,要求各高等院校的研究生院培养出社会需要的高质量人才,有些“热门”专业考生报名爆满,有些专业却无人报考。如何招收符合专业要求的合格考生,是一个值得研究的课题。研究生的入学考试分成两部分,一部分英语、政治、数学是全国统考,两门专业课往往是各高校和指导教师自定,这样就存在专业课的设置是否合理,本文首次就此问题进行了专门研究。我校93届统招研究生153名,随机抽取36名,有效的数据只有21名,本文以此21名研究生入学成绩为自变量,研究生三年中主干课程（后续主干课程）为因变量,通过多元回归和逐步回归分析,分析研究生入学成绩对后续主干课程的影响,并确定其影响程度,据此来确定近期研究生报考考生在总分数进入合格线后,对后续主干课程影响最主要的课程（入学核心课程）合格分数线的要求,且专业课的设置是否合理,从而有助于招生工作的科学化、正规化,也促进培养符合专业要求的合格人才。     

基于多元统计方法的居民健康体检数据危险因素分析

以邯郸市体检中心2014年居民的健康体检数据为依据,随机抽取了32281名居民的健康体检数据,分析了影响居民健康的危险因素,为有效的了解和掌握邯郸市居民健康状况提供帮助,为健康指导、健康干预策略的制定提供科学依据.     

高考入学成绩对后续课程影响的统计分析

本文利用多元回归和马尔柯夫链等数学模型，分析高考入学成绩对后续主于课程的影响，并确定重点专业对入学核心课程成绩的要求．     

Computer and Statistical Techniques for Planners

Journal of the Operational Research Society -     

纤维胃镜检查的统计分析

本文根据我校医院纤维胃镜检查资料,用x~2检验及主成份分析方法,讨论了胃病与职业、年龄和性别之间的关系.     

Nonmonotone and Monotone Active-Set Methods for Image Restoration,Part 2: Numerical Results

Active-set methods based on augmented Lagrangian smoothing of nondifferentiable optimization problems arising in image restoration are studied through numerical experiments. Implemented algorithms for solving both one-dimensional and two-dimensional image restoration problems are described. A new, direct way for solving the constrained optimization problem appearing in the inner iteration of the monotone algorithms is presented. Several numerical examples are included.     

多组判别分析法在恶性卵巢肿瘤B超评分诊断中的应用

为了妇科恶性肿瘤的早期诊断，对病人进行Ｂ型超声波的检查和Ｂ超评分，并把Ｂ超评分数作为初始条件，利用多组判别分析法，将它们划分为良性、过渡性或畸胎瘤和恶性等三类，是利用多元统计分析对妇科恶性肿瘤早期诊断的一种尝试．     

Statistical Methods in Research and Production

Journal of the Operational Research Society -     

数量经济学中的统计分析与预测方法

本文介绍了数量经济学中的一些基本概念，以及主成分分析在经济波动研究中的应用，并结合例子介绍了经济波动的频率域分析，详细介绍了一种区间控制计量经济模型．     

Some Statistical Results in the Combination of Forecasts

Forecasts may be combined using a minimum variance criterion to yield a composite forecast of smaller error variance than any of the components. This paper considers the sampling distributions of the weights to be attached to the components and of the error variance of the combined forecast. Confidence limits are derived for the estimates of the weights and of the variance of a composite forecast with two components. The theoretical analysis reveals that, in practice, it is doubtful whether combined forecasts offer much improvement because of the unreliability of the weight estimates.     

Constructing Robust Feedback Laws by Set Oriented Numerical Methods

In [8, 6] a numerical method for the construction of optimally stabilizing feedback laws was proposed. The method is based on a set oriented discretization of phase space in combination with graph theoretic algorithms for the computation of shortest paths in directed weighted graphs. The resulting approximate optimal value function is piecewise constant, yielding an approximate optimal feedback which might not be robust with respect to perturbations of the system. In this contribution we extend the approach to the case of perturbed control systems. Based on the concept of a multivalued game we show how to derive a directed weighted hypergraph from the original system and generalize the corresponding shortest path algorithm. The resulting optimal value function yields a robustly stabilizing approximate optimal feedback law. This note is an abbreviated version of [5]. For the proofs of the statements here we refer to the full paper. (© 2005 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

一类小样本的统计方法建模及其可视化

针对一类高维小样本数据,利用统计方法的非参数检验与偏最小二乘回归(PLS)构造小样本预测模型,实现基于Wilcoxon秩和检验的变量选择与基于PLS的变量压缩降维.并通过DNA序列分类问题实现基于统计方法的小样本数据建模与可视化,计算结果表明方法对小样本具有可行性、有效性.     

Nonparametric Statistical Methods and the Pricing of Derivative Securities

In this review paper we summarise several nonparametric methods recently applied to the pricing of financial options. After a short introduction to martingale-based option pricing theory, we focus on two possible fields of application for nonparametric methods: the estimation of risk-neutral probabilities and the estimation of the dynamics of the underlying instruments in order to construct an internally consistent model.     

Measuring the Stability of Results From Supervised Statistical Learning

Stability is a major requirement to draw reliable conclusions when interpreting results from supervised statistical learning. In this article, we present a general framework for assessing and comparing the stability of results, which can be used in real-world statistical learning applications as well as in simulation and benchmark studies. We use the framework to show that stability is a property of both the algorithm and the data-generating process. In particular, we demonstrate that unstable algorithms (such as recursive partitioning) can produce stable results when the functional form of the relationship between the predictors and the response matches the algorithm. Typical uses of the framework in practical data analysis would be to compare the stability of results generated by different candidate algorithms for a dataset at hand or to assess the stability of algorithms in a benchmark study. Code to perform the stability analyses is provided in the form of an R package. Supplementary material for this article is available online.     

分组检测方法综述

分组检测是一种经济且有效的检测算法。它在流行病学、农业及基因组学等领域有着广泛的应用。本文主要从分组检测的起源、发展及其应用着手,着重于介绍分组检测算法中的统计推断方法,并讨论了目前分组检测算法面临的一些问题和挑战。