原竹材料抗弯性能试验研究

开展了原竹材料抗弯性能试验,探究了竹材抗弯强度和弹性模量与高度、壁厚、周长的关系,采用Bootstrap法分析了竹材抗弯强度标准值,基于可靠度理论研究了原竹材料的抗弯强度设计值.结果表明:竹片抗弯加载经历较长的弹性阶段,塑性阶段较短,竹材抗弯承载力随高度增加而减小;竹片抗弯强度和弹性模量随高度增加而增大,随壁厚和周长增...     

基于Bootstrap Averaging的贝叶斯分类算法

针对单词簇上训练朴素贝叶斯文本分类器概率估计偏差较大所导致的分类精度较低问题.在使用概率分布聚类算法得到的单词簇的基础上,根据单词与簇间互信息建立有序单词子序列,采用有放回随机抽样对单词序列构造规模相当的样本集并将估计出的参数的平均值作为训练得到的最终参数对未知文本进行分类.公共文本实验数据集上的实验结果表明,提出的训练方法相对于传统的朴素贝叶斯分类器训练方法能够获得更高的分类精度且过程相对简单.     

基于开关电容的三端口DC-DC变换器

三端口直流变换器(TPC)应用于新能源供电系统与电动汽车供电系统,可将系统中原有的多个单向和双向直流变换器在电路结构上实现合并,具有结构简单、成本低、控制灵活等优点。根据TPC的分类和组成规则构建一族含有开关电容、自举电路、BUCK、BOOST电路的高压比三端口直流变换器。形成的新型三端口变换器具有压比高、结构灵活、调压范围广等特点,可以很好地应用在新能源发电应用系统中。最后对提出的新拓扑进行理论分析和实验验证。     

基于Bootstrap方法的过程能力指数区间估计

针对传统统计过程控制方法的局限性,利用Bootstrap方法构建了过程能力指数的置信区间,分别对正态分布、指数分布进行仿真计算,对3种Bootstrap置信区间进行了比较。通过实证分析验证了在小样本条件下Bootstrap置信区间的有效性。     

一种低电压高精度125 MHz采样/保持电路

介绍了一个低电压高精度的高速采样／保持电路。该电路的电源电压为1．8V，在125MHz频率时钟采样时，可达到10位以上的精度；采用栅源电压恒定的栅压自举开关，极大地减小了采样的非线性失真，同时，有效地抑制了输入信号的直流偏移；高性能增益自举的折叠式级联运算放大器减小了有限增益和不完全建立带来的误差。整个电路以0．18μm CMOS工艺库验证，功耗仅为11．2mW。     

Standard bootstrap confidence interval estimates of CPK

The interpretation of C_pk, a common measure of process capability and confidence limits for it, is based on the assumption that the process is normally distributed. The non-parametric but computer intensive method called Bootstrap is introduced and the standard Bootstrap confidence interval estimate for C_pk is defined. An initial simulation of two processes (one normal and the other highly skewed) is presented and discussed.     

ON THE STRENGTH OF DEPENDENCE OF A TIME SERIES GENERATED BY A CHAOTIC MAP

Abstract. A stochastic sequence generated by a chaotic map has extremely strong dependence in a structural sense, in that any data value may be represented exactly as a known deterministic function of any one of its antecedents. However, the range of dependence of the time series may be very short in a statistical sense - in fact, all its lagged correlations could be zero. In the present paper we study the implications of this property for two of the statistical techniques which weak dependence is often invoked to justify - asymptotic methods based on the central limit theorem, and the bootstrap. It is shown that in the case of the logistic map, the validity of these techniques depends critically on the value of the parameter governing the map. Very small alterations to the parameter value can produce dramatic changes in the strength of dependence, thereby altering the validity of even elementary statistical procedures based on asymptotic normality or resampling.     

Bagging Predictors

Bagging predictors is a method for generating multiple versions of a predictor and using these to get an aggregated predictor. The aggregation averages over the versions when predicting a numerical outcome and does a plurality vote when predicting a class. The multiple versions are formed by making bootstrap replicates of the learning set and using these as new learning sets. Tests on real and simulated data sets using classification and regression trees and subset selection in linear regression show that bagging can give substantial gains in accuracy. The vital element is the instability of the prediction method. If perturbing the learning set can cause significant changes in the predictor constructed, then bagging can improve accuracy.     

Balanced Bootstrap Joint Confidence Bands for Structural Impulse Response Functions

Constructing joint confidence bands for structural impulse response functions based on a VAR model is a difficult task because of the non‐linear nature of such functions. We propose new joint confidence bands that cover the entire true structural impulse response function up to a chosen maximum horizon with a pre‐specified probability (1?α), at least asymptotically. Such bands are based on a certain bootstrap procedure from the multiple testing literature. We compare the finite‐sample properties of our method with those of existing methods via extensive Monte Carlo simulations. We also investigate the effect of endogenizing the lag order in our bootstrap procedure on the finite‐sample properties. Furthermore, an empirical application to a real dataset is provided.     

小样本下基于Bootstrap的航程油耗预测方法

利用传统的数据分析方法预测飞机燃油消耗量需要大量的样本,针对这一问题,提出一种基于Bootstrap统计理论建立油耗预测模型的方法.基于真实的QAR(quick access recorder)数据,首先利用Bootstrap统计方法得到相关航程下油耗均值和一定置信水平下均值的置信区间,然后对多组均值和置信区间的上、下限分别进行拟合建模,能够得到油耗与航程关系模型及航程与燃油消耗带关系模型.最后,将结果分别与最小二乘法下的预测模型及2203组数据样本下的油耗模型作对比,结果表明:小样本量下的Bootstrap方法预测模型准确度较高.