Diagnostic checks for discrete data regression models using posterior predictive simulations

Model checking with discrete data regressions can be difficult because the usual methods such as residual plots have complicated reference distributions that depend on the parameters in the model. Posterior predictive checks have been proposed as a Bayesian way to average the results of goodness-of-fit tests in the presence of uncertainty in estimation of the parameters. We try this approach using a variety of discrepancy variables for generalized linear models fitted to a historical data set on behavioural learning. We then discuss the general applicability of our findings in the context of a recent applied example on which we have worked. We find that the following discrepancy variables work well, in the sense of being easy to interpret and sensitive to important model failures: structured displays of the entire data set, general discrepancy variables based on plots of binned or smoothed residuals versus predictors and specific discrepancy variables created on the basis of the particular concerns arising in an application. Plots of binned residuals are especially easy to use because their predictive distributions under the model are sufficiently simple that model checks can often be made implicitly. The following discrepancy variables did not work well: scatterplots of latent residuals defined from an underlying continuous model and quantile–quantile plots of these residuals.     

聚焦中国英语课堂:影响学生参与讨论的因素—— 一次采用定性分析法进行本土化研究的尝试

通过课堂观察及课后的学生采访,本文采用定性研究法考察了决定英语课堂上中国学生讨论情况的因素。　　本文的主要发现是：（１） 中国文化推崇的谦虚美德明显地妨碍了学生们抒发己见;（２） 学生推理能力和英语表达能力之间的鸿沟是引起表达焦虑的主要原因;（３） 教师的角色左右学生讨论中表现。基于上述发现,本文认为,课堂学习由民族性格、教育体制、口语习得心理以及教师角色所决定。因此建议,今后要注意研究课堂学习和其他影响因素之间的关系     

Semiparametric estimation of plane similarities: application to fast computation of aeronautic loads

In the big data era, it is often needed to resolve the problem of parsimonious data representation. In this paper, the data under study are curves and the sparse representation is based on a semiparametric model. Indeed, we propose an original registration model for noisy curves. The model is built transforming an unknown function by plane similarities. We develop a statistical method that allows to estimate the parameters characterizing the plane similarities. The properties of the statistical procedure are studied. We show the convergence and the asymptotic normality of the estimators. Numerical simulations and a real-life aeronautic example illustrate and demonstrate the strength of our methodology.     

An Algorithm for Unconstrained Quadratically Penalized Convex Optimization

Estimators are often defined as the solutions to data dependent optimization problems. A common form of objective function (function to be optimized) that arises in statistical estimation is the sum of a convex function V and a quadratic complexity penalty. A standard paradigm for creating kernel-based estimators leads to such an optimization problem. This article describes an optimization algorithm designed for unconstrained optimization problems in which the objective function is the sum of a non negative convex function and a known quadratic penalty. The algorithm is described and compared with BFGS on some penalized logistic regression and penalized L ^3/2 regression problems.     

An improved comorbidity summary score for measuring disease burden and predicting mortality with applications to two national cohorts

Abstract

Research involving administrative healthcare data to study patient outcomes requires the investigator to account for the patient’s disease burden in order to reduce the potential for biased results. Here we develop a comorbidity summary score based on variable importance measures derived from several statistical and machine learning methods and show it has superior predictive performance to the Elixhauser and Charlson indices when used to predict 1-year, 5-year, and 10-year mortality. We used two large Veterans Administration cohorts to develop and validate the summary score and compared predictive performance using the area under ROC curve (AUC) and the Brier score.     

Assessing student learning in the context of evaluating a chance course

This paper describes the use of different assessment methods to evaluate Chance courses in the NSF-funded Chance project. Multiple methods were used to measure changes in students' attitudes and beliefs about statistics, reasoning about probability and statistics, and ability to critique a newspaper article that reports statistical results. Samples of assessment materials and scoring rubrics used in the evaluation are included.     

高等农业职业教育《淡水养殖水化学》开展研究性学习的探索

国际 2 1世纪教育委员会的报告《教育———财富蕴藏其中》中提出面对未来社会的发展的 4种基本要求———学会认知、学会做事、学会共处、学会生存。这说明 ,在现代教育中 ,必须以学生发展为中心 ,开展研究性学习。我国实施的研究性学习就是改变我国传统的以教师为中心的教学模式 ,突出学生在学习中的主导地位。从贯彻素质教育的宗旨出发 ,在学科教学中渗透研究性学习 ,是培养学生综合能力的一种尝试和实践。     

学习型组织综合评价方法的研究

将学习型组织系统划分为学习、组织、人员、知识和信息五个子系统,对五个子系统进行了系统的分析,在此基础上建立了学习型组织综合评价指标体系,采用层次分析法确定了各指标的权重,并运用模糊综合评价法建立了学习型组织的模糊评价模型。该模型为衡量一个组织向学习型组织转变的程度提供了理论依据。     

学校认同与大学生隐性逃课的关系：有调节的中介效应分析

为了探讨学校认同对隐性逃课的影响以及学习动机的中介作用和感恩的调节作用,采用学校认同量表、学习动机问卷、隐性逃课量表和感恩问卷对1098名大学生进行施测。结果发现：1）学校认同对隐性逃课具有显著的负向预测作用;2）学习动机在学校认同与隐性逃课的关系中起部分中介作用;3）学校认同对隐性逃课的直接预测作用及学习动机在二者关系中的中介作用均受到感恩的调节。因此,学校认同通过学习动机影响大学生隐性逃课,感恩调节了学校认同与隐性逃课、学校认同与学习动机之间的关系。关键词：学校认同;隐性     

把高校建成传播先进文化的坚强阵地

如何才能把高校建成传播先进文化的坚强阵地,这既是一个理论问题,也是一个实践问题.首先,必须深刻认识高校文化建设的战略意义,始终坚持先进文化发展的前进方向.其次,必须抓住关键,发挥学生的主体作用和教师的主导作用.同时,必须加强领导,齐抓共管,课堂内外,全面渗透,从而营造具有浓厚文化氛围的育人环境,使高校成为传播先进文化的坚强阵地.