On estimating model complexity and prediction errors in multivariate calibration: generalized resampling by random sample weighting (RSW)

The present paper focuses on determining the number of PLS components by using resampling methods such as cross validation (CV), Monte Carlo cross validation (MCCV), bootstrapping (BS), etc. To resample the training data, random non‐negative weights are assigned to the original training samples and a sample‐weighted PLS model is developed without increasing the computational burden much. Random weighting is a generalization of the traditional resampling methods and is expected to have a lower risk of getting an insufficient training set. For prediction, only the training samples with random weights less than a threshold value are selected to ensure that the prediction samples have less influence on training. For complicated data, because the optimal number of PLS components is often not unique or readily distinguished and there might exist an optimal region of model complexity, the distribution of prediction errors can be more useful than a single value of root mean squared error of prediction (RMSEP). Therefore, the distribution of prediction errors are estimated by repeated random sample weighting and used to determine model complexity. RSW is compared with its traditional counterparts like CV, MCCV, BS and a recently proposed randomization test method to demonstrate its usefulness. Copyright © 2010 John Wiley & Sons, Ltd.     

Bootstrapping the change-point of a hazard rate

This paper concerns the asymptotic validity of the bootstrap method in a non-regular model. Specifically, it is shown that the parametric bootstrap of the change-point parameter in the change-point hazard rate model works.     

Graphical Criticism: Some Historical Notes

This article proposes a bootstrap local bandwidth selector for estimating nonparametric additive models. The selector is derived from a bootstrap approximation of the conditional mean squared error, based on a wild bootstrap resampling scheme applied to the estimated residuals. The selector is computed exactly (without involving Monte Carlo approximations) and in practice can be evaluated for many additive estimation methods, including backfitting (bivariate), marginal integration, and mixed methods. We study the consistency of the bootstrap approximation and also carry out an empirical simulation study to explore the performance of the proposed selector in comparison with others. The graphical tool SiZer Map enables us to make meaningful comparisons between local and global selectors.     

二次耦合直流输出的金属氧化物TFT行驱动电路

为了弥补现有氧化物TFT的行驱动电路输出模块在功率消耗、响应速度、输出摆幅等方面的不足,提出了基于二次耦合的直流输出模块,并由此研究设计新的行驱动电路拓扑。仿真结果表明,该输出模块具有驱动能力强、响应速度快等优点。最后,基于刻蚀阻挡层(ESL)结构的氧化物TFT工艺,在玻璃衬底上成功制备了该行驱动电路,实测单级功耗为325μW。     

样本函数条件极值中减低偏差的方法

对样本函数条件极值中偏差项的阶进行了分析,探讨了减低偏差项的方法,分析表明古典折刀法、减-d折刀法均不能减低偏差项;在此基础上,提出了减低偏差项的自助法,并论证了在均方误差意义下,θnab是一种较优的估计.     

经验似然重抽样下回归模型的Bootstrap逼近

本文提出用经验似然重抽样来bootstrap逼近线性回归模型中的学生化最小二乘估计．我们证明了该方法具有一般s－2项Edgeworth展开，它是二阶相合的而且比经典的方法损失更小．     

基于自助法和核密度估计的膳食暴露评估模型

针对现阶段我国食品卫生安全保障体系的不完善,建立了基于自助法和核密度估计的膳食暴露评估模型.首先给出一种食物摄入量的抽样调查方案,并按污染物含量对特征人群进行模糊区间分类,建立特征人群每人每天食物摄入量的截尾正态分布模型.然后通过自助法重复抽样,弥补偶然抽查数据数量上的不足,提出了用对数正态分布核密度来估计污染物分布,并对模型进行了检验.在此基础上,建立了污染物摄入量模型,利用Gauss-Legendre求积公式求出其99.999%的右分位点,并且采用模糊匹配技术解决了数据不配套问题.最后对模型进行了讨论,给出了一种思路.     

Spatial contagion between financial markets: a copula‐based approach

A method is proposed for defining and investigating spatial contagion between two financial markets X and Y by using the information contained in their copula. A practical illustration of the introduced method is also given by examining the presence of contagion among two European stock indices (namely, FTSE 100 and DAX). Copyright © 2009 John Wiley & Sons, Ltd.     

Error inference for nonparametric regression

This study examines means for inferring the distribution of the error in nonparametric regression. The central objective is to develop confidence intervals for nonparametric regression. Our computational study would seem to affirm that our methods are potentially useful in cases of small sample size or heterogeneously distributed error. Theoretical developments offer sufficient conditions for asymptotic normality.This work was undertaken while Dr. Rutherford was with the University of Arizona. It was supported in part by NSF grant DPP 82-19439.