Improvement of parameter estimation for non-linear hysteretic systems with slip by a fast Bayesian bootstrap filter

Modeling and identification of non-linear hysteretic systems are widely encountered in the structural dynamics field, especially for the hysteresis with slip. A model, called SL model, which can describe the pinching of most practical hysteresis loops perfectly was proposed by Baber and Noori (J. Eng. Mech. 111 (1985) 1010). A method of estimating the parameters of SL model on the basis of input-output data based on bootstrap filter was proposed by the writers. Bootstrap filter is a filtering method based on Bayesian state estimation and Monte Carlo method, which has the great advantage of being able to handle any functional non-linearity and system and/or measurement noise of any distribution. The standard bootstrap filter, however, is not time efficient, i.e., it is very time consuming and is not suitable for real-time applications. In this paper, previous work by the writers is extended to do the parameter estimation of SL model by a fast Bayesian bootstrap filtering technique. Simulation results are presented to demonstrate the performance of the algorithm.     

Polynomial Regression with Censored Data based on Preliminary Nonparametric Estimation

Consider the polynomial regression model , where σ²(X)=Var(Y|X) is unknown, and ε is independent of X and has zero mean. Suppose that Y is subject to random right censoring. A new estimation procedure for the parameters β₀,...,β _p is proposed, which extends the classical least squares procedure to censored data. The proposed method is inspired by the method of Buckley and James (1979, Biometrika, 66, 429–436), but is, unlike the latter method, a noniterative procedure due to nonparametric preliminary estimation of the conditional regression function. The asymptotic normality of the estimators is established. Simulations are carried out for both methods and they show that the proposed estimators have usually smaller variance and smaller mean squared error than the Buckley–James estimators. The two estimation procedures are also applied to a medical and an astronomical data set.     

多个偏正态总体共同位置参数的Bootstrap置信区间

该文基于Bootstrap 方法研究多个偏正态总体共同位置参数的区间估计和假设检验问题.首先,分别给出未知参数的矩估计和极大似然估计.其次,将徐礼文[1]对多个正态总体共同均值的探讨推广到多个偏正态总体,进而构造共同位置参数的Bootstrap 置信区间和Bootstrap检验统计量.Monte Carlo模拟结果表明...     

对Bootstrap方法的自助抽样的改进

Bootstrap方法是一种常用的统计推断方法,它的无先验性,以及计算过程中只需要实际的观测数据,使得其可方便的应用于实际的数据处理之中。对连续情形而言,Bootstrap本身的计算特性限制了自助样本的生成范围,即只能从原样本再采样,从而使得自助分布产生偏离,无法渐近于真实情形。鉴于此不足,本文提出了改进的Bootstrap方法,它通过对自助样本生成范围的拓展,克服了这一不足,获得了更加精确的参数估计。     

Model Search by Bootstrap “Bumping”

Abstract

We propose a bootstrap-based method for enhancing a search through a space of models. The technique is well suited to complex, adaptively fitted models—it provides a convenient method for finding better local minima and for resistant fitting. Applications to regression, classification, and density estimation are described. We also provide results on the asymptotic behavior of bumping estimates.     

Testing for Hermite Rank in Gaussian Subordination Processes

Statistical inference for time series with long-range dependence is often based on the assumption of Gaussian subordination X_t = G(Z_t). Although the Hermite rank m of G plays an essential role for statistical inference in these situations, the question of estimating m or of testing hypotheses about the Hermite rank has not been addressed in the literature. In this article, a method is introduced for testing H₀: m = 1 against H₁: m > 1. This allows for deciding whether inference based on the usual assumption of m = 1 is appropriate. Simulations and data examples illustrate the method.     

Correlations,hierarchies and networks of the world’s automotive companies

We investigate, within the scope of econophysics, the correlations, hierarchies and networks of the world’s automotive companies over the 2003–2010 period by using the concept of a minimal spanning tree (MST) and hierarchical tree (HT). We derive a hierarchical organization and construct the MSTs and HTs for the 2003–2010 period and illustrate how the MSTs and their associated HTs developed over time. These periods are divided into two subperiods, such as 2003–2006 and 2007–2010, in order to test various time-windows and understand the temporal evolution of the correlation structure over time. We perform the bootstrap techniques to investigate a value of the statistical reliability to the links of the MSTs. We also use average linkage cluster analysis (ALCA) to observe the cluster structure more clearly in HTs. From the structural topologies of these trees, we identify different clusters of companies according to their geographical proximity and economic ties. Our results show that some companies are more important within the network, due to a tighter connection with other companies. We also find that these important companies play a predominant role in the world’s automotive industry.     

Bootstrap confidence interval for a correlation curve

A correlation curve measures the strength of the association between two variables locally at different values of x. The purpose of this study is to obtain point-wise confidence intervals for a correlation curve using wild bootstrap techniques. Empirical coverage probabilities are found to be close to the specified nominal level. Bootstrapping is an attractive alternative to confidence intervals based on asymptotic expressions that have slow rate of convergence.     

基于灰色模型和Bootstrap理论的大规模定制质量控制方法研究

针对大规模定制生产中样本量小而无法直接应用传统控制图判断过程状态的问题,提出基于灰色模型和Bootstrap理论的质量控制方法.首先采用灰色模型,以现有样本为基础进行数据分析,适当扩展样本量;然后基于Bootstrap统计推断方法获得统计量的经验分布;最后建立样本均值和极差控制图,对生产过程的状态进行分析判断.研究表明,新方法可用于大规模定制生产尤其是新型号生产初期的质量预测和控制.     

统计方法在大学英语四级成绩分析中的应用

本文以本校2005级本科生为调查对象,考察影响大学生四级考试（College English Test Band Four,简称CET-4）CET-4成绩的因素。采用Bootstrap方法与T检验方法,分析得出：来自城市和农村的大学生CET-4成绩存在差异;大一大二四学期英语水平不同的人CET-4成绩存在显著差异;文、理科大学生CET-4成绩也存在差异。进一步,我们运用多元统计中回归分析的方法建立了CET-4成绩的回归模型,并利用Logistic模型对四级通过率进行了预测。