A decision theoretic approach to change point estimation for binomial CUSUM control charts

Detecting when the process has changed is a classical problem in sequential analysis and is an important practical issue in statistical process control. This article is concerned about the binomial cumulative sum (CUSUM) control chart, which is extensively applied to industrial process control, health care, public health surveillance, and other fields. For the binomial CUSUM, a maximum likelihood estimator has been proposed to estimate the change point. In our article, following a decision theoretic approach, we develop a new estimator that aims to improve the existing methods. For interval estimation, we propose a parametric bootstrap procedure to construct the confidence set of the change point. We compare our proposed method with the maximum likelihood estimator and Page's last zero estimator in terms of mean squared error by simulations. We find that the proposed method gives more unbiased and robust results than the existing procedures under various parameter designs. We analyze jewelry manufacturing data for illustration.     

Bootstrap-based bandwidth choice for log-periodogram regression

Abstract.  The choice of the bandwidth in the local log-periodogram regression is of crucial importance for estimation of the memory parameter of a long memory time series. Different choices may give rise to completely different estimates, which may lead to contradictory conclusions, for example about the stationarity of the series. We propose here a data-driven bandwidth selection strategy that is based on minimizing a bootstrap approximation of the mean-squared error (MSE). Its behaviour is compared with other existing techniques for optimal bandwidth selection in a MSE sense, revealing its better performance in a wider class of models. The empirical applicability of the proposed strategy is shown with two examples: the widely analysed in a long memory context Nile river annual minimum levels and the input gas rate series of Box and Jenkins.     

基于智能功率模块的开关磁阻电动机功率电路研究

介绍了一种新型基于智能功率模块开关磁阻电动机功率电路,研究了日本三菱公司生产的第三代智能功率模块PS21564的工作原理及其特有的自举充电电路的充电过程及其可靠实用的电路连接方式,并给出仿真结果,证明了此种电路是一种方便可行的开关磁阻电机功率电路.     

An Efficient Method To Estimate Bagging's Generalization Error

Bagging (Breiman, 1994a) is a technique that tries to improve a learning algorithm's performance by using bootstrap replicates of the training set (Efron & Tibshirani, 1993, Efron, 1979). The computational requirements for estimating the resultant generalization error on a test set by means of cross-validation are often prohibitive, for leave-one-out cross-validation one needs to train the underlying algorithm on the order of m times, where m is the size of the training set and is the number of replicates. This paper presents several techniques for estimating the generalization error of a bagged learning algorithm without invoking yet more training of the underlying learning algorithm (beyond that of the bagging itself), as is required by cross-validation-based estimation. These techniques all exploit the bias-variance decomposition (Geman, Bienenstock & Doursat, 1992, Wolpert, 1996). The best of our estimators also exploits stacking (Wolpert, 1992). In a set of experiments reported here, it was found to be more accurate than both the alternative cross-validation-based estimator of the bagged algorithm's error and the cross-validation-based estimator of the underlying algorithm's error. This improvement was particularly pronounced for small test sets. This suggests a novel justification for using bagging—more accurate estimation of the generalization error than is possible without bagging.     

Toward using confidence intervals to compare correlations.

Confidence intervals are widely accepted as a preferred way to present study results. They encompass significance tests and provide an estimate of the magnitude of the effect. However, comparisons of correlations still rely heavily on significance testing. The persistence of this practice is caused primarily by the lack of simple yet accurate procedures that can maintain coverage at the nominal level in a nonlopsided manner. The purpose of this article is to present a general approach to constructing approximate confidence intervals for differences between (a) 2 independent correlations, (b) 2 overlapping correlations, (c) 2 nonoverlapping correlations, and (d) 2 independent R2s. The distinctive feature of this approach is its acknowledgment of the asymmetry of sampling distributions for single correlations. This approach requires only the availability of confidence limits for the separate correlations and, for correlated correlations, a method for taking into account the dependency between correlations. These closed-form procedures are shown by simulation studies to provide very satisfactory results in small to moderate sample sizes. The proposed approach is illustrated with worked examples. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Bootstrap与模糊聚类在管道泄漏检测的研究

管道泄漏信息缺失对可靠提取小泄漏与工况变化所产生的负压波模式的特征一直是一瓶颈，也是降低系统误报警率的关键因素之一。本文利用小样本统计的Bootstrap方法，对观测样本进行模拟采样并进行统计分析，在此基础上，对管道不同工况提取特征．并用模糊聚类分析的方法分类管道小泄漏与工况变化的运行模式。仿真实验表明该方法应用在管道泄漏诊断中是简单实用和可行的。     

嵌入式无线局域网适配器的设计与实现

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,该文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.     

Accounting for Lag Order Uncertainty in Autoregressions: the Endogenous Lag Order Bootstrap Algorithm

Conventional asymptotic and bootstrap methods for finite-order autoregressive models condition on the estimated lag order of the model as though it were known to be the true lag order. Even if the order is estimated correctly, this procedure ignores the sampling uncertainty about the lag order estimate and may result in spurious inferences. In this paper an appropriately modified bootstrap algorithm is introduced that reflects the true extent of sampling uncertainty in the regression estimates. This endogenous lag order bootstrap algorithm recognizes that lag order selection is an integral part of the sampling procedure by re-estimating the lag order in each bootstrap iteration. It is suggested that the endogenous lag order bootstrap algorithm should routinely replace the standard bootstrap algorithm in applications. Monte Carlo simulations show that ignoring lag order uncertainty may seriously undermine the coverage accuracy of bootstrap confidence intervals for vector autoregression impulse response estimates. Endogenizing the lag order choice is shown to improve coverage accuracy in small samples at negligible additional computational cost. As the lag order uncertainty declines in large samples, the performance of the endogenous lag order interval converges to that of the standard interval.     

高压线性电流传感器IR2175及其应用

IR2175是IR(International Rectifier)公司生产的高压线性电流传感器，它不用通过AID转换便可直接输出PWM数字信号，从而简化了电机控制电路的电流检测和保护操作，可方便地用于电机驱动系统中。文中介绍了IR2175的结构特点、引脚功能和主要特性，给出了其典型应用电路。     

Mediation in experimental and nonexperimental studies: New procedures and recommendations.

Mediation is said to occur when a causal effect of some variable X on an outcome Y is explained by some intervening variable M. The authors recommend that with small to moderate samples, bootstrap methods (B. Efron & R. Tibshirani, 1993) be used to assess mediation. Bootstrap tests are powerful because they detect that the sampling distribution of the mediated effect is skewed away from 0. They argue that R. M. Baron and D. A. Kenny's (1986) recommendation of first testing the X → Y association for statistical significance should not be a requirement when there is a priori belief that the effect size is small or suppression is a possibility. Empirical examples and computer setups for bootstrap analyses are provided. (PsycINFO Database Record (c) 2010 APA, all rights reserved)