非参数回归模型均值函数结构变点的检测与应用

本文将一类系统参数变点检测问题转化为非参数回归模型均值函数结构变点的检测问题.针对当非参数模型均值函数跃度的长期均值为零时,残量累积和(cumulative sum,CUSUM)统计量无效的问题,首先利用均值函数的核估计构造新统计量,给出了原假设和备择假设下统计量的极限分布;进一步构造Bootstrap检验,证明了Bootstrap检验的一致性;最后以模拟结果表明新方法明显优于已有的方法,并应用于两类实际数据分析,说明方法的有效性.     

脑卒中信息管理系统权限控制的设计与实现

随着医疗信息技术和互联网的飞速发展,医疗信息资源的安全越来越备受关注,权限管理为解决信息系统安全性问题提供了重要保障,为防止非法获得或破坏信息起着重要的作用;脑卒中信息管理系统存放大量脑卒中患者的治疗信息,为医生发现其潜在的疾病提供依据;针对脑卒中信息管理系统复杂的权限控制需求,提出了一种权限控制方法,采用SpringMVC+Hibernate后台框架,Bootstrap前台框架技术,以及B/S结构软件设计中的精粒度权限管理思路,实现了脑卒中软件系统中的权限管理;同时将权限控制设计为一套与业务无关的权限管理组件,该组件不需要做代码级的更改可以轻松的移植到其它Web系统中;结果表明,系统能够满足权限控制需求,具有良好的可操作性、灵活性和移植性。     

Estimating residual variance in random forest regression

Random forest, a data-mining technique which uses multiple classification or regression trees, is a popular algorithm used for prediction. Inference and goodness-of-fit assessment, however, may require an estimator of variability; in many applications the residual variance is of primary interest. This paper proposes two estimators of residual variance for random forest regression that take advantage of byproducts of the algorithm. The first estimator is based on the residual sum of squares from a random forest fit and uses a bootstrap bias correction. The second estimator is a difference-based estimator that uses proximity measures as weights. The estimators are evaluated through Monte Carlo simulations. Applications of the methods to the problem of assessing the relative variability of males and females on cognitive and achievement tests are discussed, and the methods are applied to estimate the residual variance in test scores for male and female students on the mathematics portion of the 2007 Arizona Instrument to Measure Standards.     

On the efficiency of bootstrap method into the analysis contingency table

The bootstrap method is a computer intensive statistical method that is widely used in performing nonparametric inference. Categorical data analysis, in particular the analysis of contingency tables, is commonly used in applied field. This work considers nonparametric bootstrap tests for the analysis of contingency tables. There are only a few research papers which exploit this field. The p-values of tests in contingency tables are discrete and should be uniformly distributed under the null hypothesis. The results of this article show that corresponding bootstrap versions work better than the standard tests. Properties of the proposed tests are illustrated and discussed using Monte Carlo simulations. This article concludes with an analytical example that examines the performance of the proposed tests and the confidence interval of the association coefficient.     

Detecting change-points in Markov chains

Markov chains provide a flexible model for dependent random variables with applications in such disciplines as physics, environmental science and economics. In the applied study of Markov chains, it may be of interest to assess whether the transition probability matrix changes during an observed realization of the process. If such changes occur, it would be of interest to estimate the transitions where the changes take place and the probability transition matrix before and after each change. For the case when the number of changes is known, standard likelihood theory is developed to address this problem. The bootstrap is used to aid in the computation of p-values. When the number of changes is unknown, the AIC and BIC measures are used for model selection. The proposed methods are studied empirically and are applied to example sets of data.     

About the relevance of roughness parameters used for characterizing worn femoral heads

This study aims to contribute to the definition of a methodology, which can help to select a relevant roughness parameter with a view to describing the topography of orthopaedic bearing surfaces. In this investigation, the surface topography of a retrieved titanium alloy (TA6V) femoral head was characterized using visual inspection, optical microscopy and three-dimensional contacting profilometry. A numerical analysis of roughness measurements was then undertaken to assess in a first step the values of different roughness parameters of interest found in papers dealing with the topography of orthopaedic bearing surfaces. In a second step, the Analysis of Variance (ANOVA) and the Computer-Based Bootstrap Method were combined to determine statistically, and without preconceived opinion, which of those parameters is the most relevant to describe the different investigated worn regions of the studied femoral head.     

Estimating probabilistic fatigue of Nitinol with scarce samples

Current work estimates probabilistic fatigue life efficiently with scarce samples. The underlying idea of the estimation is to approximate the cumulative distribution function of the fatigue life in a transformed space using a third order polynomial subject to monotonicity constraint. The variations associated with the estimated quantiles are quantified using bootstrap. The proposed approach is validated on a data obtained from literature. It is observed that the life quantiles with reasonable accuracy can be estimated even with 10 samples. Finally, the probabilistic fatigue of Nitinol in austenitic condition is obtained with limited experiments.     

Bootstrap方法在Jensen Alpha估计中的运用

詹森差别表现指数(Jensen Alpha)是使用较多的一种评价投资组合业绩的指标。进一步分析Bootstrap方法的基本原理,探讨将Bootstrap运用于Jensen Alpha估计的具体过程,从而解决了运用普通最小二乘法(OLS)估计Jensen Alpha存在的缺陷,提高了Jensen Alpha估计的稳健性,对于投资组合业绩的评价具有一定的参考意义。     

Modelling commuting time in the US: Bootstrapping techniques to avoid overfitting

The research on commuting has emerged in recent decades, but the issue of whether the empirical techniques used are appropriate has not been analysed. Thus, results from prior research could be based on non-accurate models, leading to misleading conclusions. We apply an algorithmic approach based on bootstrapping, variable selection, and mean absolute prediction errors, which is designed to avoid overfitting. Using the American Time Use Survey, we find that models with a reduced set of explanatory variables have similar accuracy to standard econometric models. Our results shed light on the importance of determining whether models can be overfitted.