Two-phase nonlinear regression with smooth transition

A method of estimating parameters of twophase nonlinear regression with smooth transition between phases is described. It consists of two stages, both utilizing the least square fit. In the first one, each phase is fitted separately and, simultaneously, a transition point is determined. In the second stage, the two phases are joined smoothly by a proper transition function, which depends on the transition point chosen by the grid search. The practical aspects of the method proposed are demonstrated on the data concerning the soil bulk density in dependence on the soil water content.     

On hsu's model in regression analysis

The paper examplifies with Hsu’s model a general pattern as how to derive results of variance component estimation from well known results on mean estimation, as far as linear model theory is concerned. This ’ dispersion-mean-correspondence‘provides new and short proofs for various theorems from the literature, concerning unbiased invariant quadratic estimators with minimum BAYES risk or minimum variance. For pure variance component models, unbiased non-negative quadratic estimability is characterized in terms of the design matrices.     

A consistent test for heteroscedasticity in semi-parametric regression with nonparametric variance function based on the kernel method

It is important to detect the variance heterogeneity in regression models. Heteroscedasticity tests have been well studied in parametric and nonparametric regression models. This paper presents a consistent test for heteroscedasticity for nonlinear semi-parametric regression models with nonparametric variance function based on the kernel method. The properties of the test are investigated through Monte Carlo simulations. The test methods are illustrated with a real example.     

On a nonlinear Birnbaum–Saunders model based on a bivariate construction and its characteristics

Abstract

The Birnbaum-Saunders (BS) distribution is an asymmetric probability model that is receiving considerable attention. In this article, we propose a methodology based on a new class of BS models generated from the Student-t distribution. We obtain a recurrence relationship for a BS distribution based on a nonlinear skew–t distribution. Model parameters estimators are obtained by means of the maximum likelihood method, which are evaluated by Monte Carlo simulations. We illustrate the obtained results by analyzing two real data sets. These data analyses allow the adequacy of the proposed model to be shown and discussed by applying model selection tools.     

Partially adaptive estimation of nonlinear models via a normal mixture

This paper extends the partially adaptive method Phillips (1994) provided for linear models to nonlinear models. Asymptotic results are established under conditions general enough they cover both cross-sectional and time series applications. The sampling efficiency of the new estimator is illustrated in a small Monte Carlo study in which the parameters of an autoregressive moving average are estimated. The study indicates that, for non-normal distributions, the new estimator improves on the nonlinear least squares estimator in terms of efficiency.     

基于多层神经网络的非线性系统预测

针对一类非线性动态系统,提出了一种新的基于多层神经网络的自适应预测器,并对基于神经网络的预测机理进行了分析。该预测器由两个同构的多层神经网络实现,其中一个称为学习网络(LN),另一个称为预测网络(PN)。彷真结果表明,速种基于多层神经网络的预测嚣对于恒定参数的非线性系统是有效的,从而为解决非线性系统预测问题提供了一条可行的途径。     

一类非线性动力学系统的变结构混沌控制

对于Ｄｕｆｆｉｎｇ模型非线性动力学系统,当系统处于混沌时,引入带扰动补偿的变结构控制律,能使系统快速到达滑动模态,并沿着滑动模态渐进到达系统的平衡点,从而保证了混沌控制的稳定和鲁棒性。理论分析和仿真验证表明了混沌控制算法的有效性。     

在单晶片CdS_xSe_（1－x）上的非线性特性的研究

提供了一种研究非线性晶体透过率特性的实验（双光束方法）和理论方法。使用这一方法可记录到厚度为１μｍ的单晶片上的时间为１－μｓ范围内的透过率变化，并用计算机模拟了这一过程。     

一类非线性Schrodinger方程Cauchy问题整体解的不存在性

研究一类非线性Schrodinger方程Cauchy问题整体解的不存在性，在初始能量非负和小于零两种情况下，都得到了问题整体解不存在的条件。从而推广了1997GlasseyR·T．的结果。