金融资产收益跳跃行为的微观机制

文章发展了一个均衡模型,从微观机理角度解释了为什么资产价格会发生不频繁的跳跃行为.在该模型中,投资者学习不可直接观测的、真实的经济系统状态并为此支付成本的行为诱发了资产价格的跳跃行为.而投资者是否选择学习的行为则取决于投资者的偏好参数和收入的条件波动率.     

Feature selection and machine learning with mass spectrometry data for distinguishing cancer and non-cancer samples

This is a comparative study of various clustering and classification algorithms as applied to differentiate cancer and non-cancer protein samples using mass spectrometry data. Our study demonstrates the usefulness of a feature selection step prior to applying a machine learning tool. A natural and common choice of a feature selection tool is the collection of marginal p-values obtained from t-tests for testing the intensity differences at each m/z ratio in the cancer versus non-cancer samples. We study the effect of selecting a cutoff in terms of the overall Type 1 error rate control on the performance of the clustering and classification algorithms using the significant features. For the classification problem, we also considered m/z selection using the importance measures computed by the Random Forest algorithm of Breiman. Using a data set of proteomic analysis of serum from ovarian cancer patients and serum from cancer-free individuals in the Food and Drug Administration and National Cancer Institute Clinical Proteomics Database, we undertake a comparative study of the net effect of the machine learning algorithm–feature selection tool–cutoff criteria combination on the performance as measured by an appropriate error rate measure.     

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.     

计算机辅助英语阅读课程设计的若干问题

本文从计算机辅助语言学习（ Ｃ Ａ Ｌ Ｌ） 课程的设计思想出发,分析和讨论有关机助英语阅读课程设计的若干问题,如阅读能力,教学方法,学习的评价等,以揭示机助英语阅读课程乃至机助语言学习课程设计的一些规律和原则     

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。