一个新混沌纠缠系统的Hopf分岔分析

文章基于混沌纠缠方法构造了一个新的混沌系统,通过理论和数值分析验证了该系统存在混沌吸引子．此外,利用非线性动力学理论分析了该系统平衡点的稳定性以及Hopf分岔的存在性和稳定性．经过计算系统在平衡点的第一Lyapunov系数判断Hopf分岔的方向及其稳定性,最后进行数值仿真验证理论分析的正确性．     

千吨级注塑机十字头轴孔配合的接触分析

针对曲肘式注塑机短连杆与十字头轴孔配合接触部分容易发生疲劳失效状况,以短连杆与十字头轴孔配合接触 对为研究对象,应用有限元法通过等效简化建立有限元接触模型。充分考虑轴孔配合表面接触微粒间的相互作用影响, 运用大型有限元软件ANSYS中的非线性有限元仿真方法,数值计算并分析了不同作用力、不同摩擦系数下接触面的接 触状态。结果表明,轴孔配合在轴受作用力变形后,接触面出现应力集中,接触应力与摩擦应力由轴外侧往内侧逐渐递 增,为分析轴孔配合接触失效提供了一定的依据。     

Bivariate Tail Dependence and the Generation of Multivariate Extreme Value Distributions

We define, in a probabilistic way, a parametric family of multivariate extreme value distributions. We derive its copula, which is a mixture of several complete dependent copulas and total independent copulas, and the bivariate tail dependence and extremal coefficients. Based on the obtained results for these coefficients, we propose a method to build multivariate extreme value distributions with prescribed tail/extremal coefficients. We illustrate the results with examples.     

Time-of-Day Electricity Consumption Response to Temperature and the Ownership of Air Conditioning Appliances

This article is concerned with the endogeneity of air conditioning appliance ownership in the consumption of electricity by time of day (TOD). Both TOD consumption and air conditioning appliance ownership depend on temperature level and on household responsiveness to temperature variation, as well as on other measured household characteristics. The article presents an appropriate econometric methodology and applies it to obtain estimates and perform tests based on data from Southern California Edison's Residential TOD Pricing Experiment. The authors emphasize the implications for estimated consumption price elasticities.     

Marginal Regression Model with Time-Varying Coefficients for Panel Data

In this article, we formulate a class of semiparametric marginal means models with a mixture of time-varying and time-independent parameters for analyzing panel data. For inference about the regression parameters, an estimation procedure is developed and asymptotic properties of the proposed estimators are established. In addition, some tests are presented for investigating whether or not covariate effects vary with time. The finite-sample behavior of the proposed methods is examined in simulation studies, and the data from an AIDS clinical trial study are used to illustrate the methodology.     

Consistency of Ridge Function Fields for Varying Nonparametric Regression

A nonparametric regression model proposed in Pelletier and Frouin (2006 Pelletier , B. , Frouin , R. ( 2006 ). Remote sensing of phytoplankton chlorophyll-a concentration by ridge function fields . App. Optics 45 : 784 – 798 . [Google Scholar]) as a solution to the geophysical problem of ocean color remote sensing is studied. The model, called ridge function field, combines a regression estimate in the form of a superposition of ridge functions, or which is equivalent to a neural network, with the idea pertaining to varying-coefficients models, where the parameters of a parametric family are allowed to vary with other variables. Under mild assumptions on the underlying distribution of the data, the strong universal consistency of the least-squares ridge function fields estimate is established.     

Evaluating the Significance Test When the Correlation Coefficient is Different from Zero in the Test of Hypothesis

Sample size and correlation coefficient of populations are the most important factors which influence the statistical significance of the sample correlation coefficient. It is observed that for evaluating the hypothesis when the observed value of the correlation coefficient's r is different from zero, Fisher's Z transformation may be incorrect for small samples especially when population correlation coefficient ρ has big values. In this study, a simulation program has been generated for to illustrate how the bias in the Fisher transformation of the correlation coefficient affects estimate precision when sample size is small and ρ has big value. By the simulation results, 90 and 95% confidence intervals of correlation coefficients have been created and tabled. As a result, it is suggested that especially when ρ is greater than 0.2 and sample sizes of 18 or less, Tables 1 and 2 can be used for the significance test in correlations.     

A Simple Method Using CuScore to Monitor Changes in ARMA Coefficients

This paper suggests a simple nonmetric method for smoothing time series data. The smoothed series is the closest polytone curve to the presmoothed series in terms of least sum of absolute deviations. The method is exemplified on several seasonally adjusted series in order to estimate their trend component.     

Testing equality of correlation coefficients for paired binary data from multiple groups

Paired binary data arise naturally when paired body parts are investigated in clinical trials. One of the widely used models for dealing with this kind of data is the equal correlation coefficients model. Before using this model, it is necessary to test whether the correlation coefficients in each group are actually equal. In this paper, three test statistics (likelihood ratio test, Wald-type test, and Score test) are derived for this purpose. The simulation results show that the Score test statistic maintains type I error rate and has satisfactory power, and therefore is recommended among the three methods. The likelihood ratio test is over conservative in most cases, and the Wald-type statistic is not robust with respect to empirical type I error. Three real examples, including a multi-centre Phase II double-blind placebo randomized controlled trial, are given to illustrate the three proposed test statistics.