Local Influence Analysis in AB–BA Crossover Designs

The aim of this article is to develop methodology for detecting influential observations in crossover models with random individual effects. Various case‐weighted perturbations are performed. We obtain the influence of the perturbations on each parameter estimator and on their dispersion matrices. The obtained results exhibit the possibility to obtain closed‐form expressions of the influence using the residuals in mixed linear models. Some graphical tools are also presented.     

Influence diagnostics in a vector autoregressive model

In this paper, we use a likelihood approach and the local influence method introduced by Cook [Assessment of local influence (with discussion). J Roy Statist Soc Ser B. 1986;48:133–149] to study a vector autoregressive (VAR) model. We present the maximum likelihood estimators and the information matrix. We establish the normal curvature and slope diagnostics for the VAR model under several perturbation schemes and use the Monte Carlo method to obtain benchmark values for determining the influence of directional diagnostics and possible influential observations. An empirical study using the VAR model to fit real data of monthly returns of IBM and S&P500 index illustrates the effectiveness of our proposed diagnostics.     

Stepwise local influence in generalized autoregressive conditional heteroskedasticity models

Detection of outliers or influential observations is an important work in statistical modeling, especially for the correlated time series data. In this paper we propose a new procedure to detect patch of influential observations in the generalized autoregressive conditional heteroskedasticity (GARCH) model. Firstly we compare the performance of innovative perturbation scheme, additive perturbation scheme and data perturbation scheme in local influence analysis. We find that the innovative perturbation scheme give better result than other two schemes although this perturbation scheme may suffer from masking effects. Then we use the stepwise local influence method under innovative perturbation scheme to detect patch of influential observations and uncover the masking effects. The simulated studies show that the new technique can successfully detect a patch of influential observations or outliers under innovative perturbation scheme. The analysis based on simulation studies and two real data sets show that the stepwise local influence method under innovative perturbation scheme is efficient for detecting multiple influential observations and dealing with masking effects in the GARCH model.     

Linear Regression for Panel With Unknown Number of Factors as Interactive Fixed Effects

In this paper, we study the least squares (LS) estimator in a linear panel regression model with unknown number of factors appearing as interactive fixed effects. Assuming that the number of factors used in estimation is larger than the true number of factors in the data, we establish the limiting distribution of the LS estimator for the regression coefficients as the number of time periods and the number of cross‐sectional units jointly go to infinity. The main result of the paper is that under certain assumptions, the limiting distribution of the LS estimator is independent of the number of factors used in the estimation as long as this number is not underestimated. The important practical implication of this result is that for inference on the regression coefficients, one does not necessarily need to estimate the number of interactive fixed effects consistently.     

简并能级高阶微扰修正与严格解

通过一个在一阶修正中简并部分解除的例，利用表象变换方法，给出前三阶微扰修正的结果，与严格解的级数展开式比较，两者结果是一致的。同时表明在一阶修正中分裂出来的简并态，作为中间态是如何在高阶（ｎ≥３）修正中提供贡献的。     

一类具扰动Chandrasekhar H-方程的可积正解

本文讨论了迁移理论和气体动力学中一类具扰动的 Ｃｈａｎｄｒａｓｅｋｈａｒ Ｈ－方程：这里Ｇ是Ｒ～ｎ中的有界闭集，为已知函数，λ为常数。本文研究并得到了方程（０．６）可积正解的存在性、个数、逼近性、稳定性，改进和发展了〔４〕［５］［７］的结果。     

三维限制TSP的退火模拟算法

给出了一种具有全局优化特性的三维限制TSP的求解方法,研究了针对三维限制TSP的退火模拟模型的建立、扰动的形成和算法参数的选取,最后给出了实验例子。     

Local influence for generalized linear mixed models

The authors describe a method for assessing model inadequacy in maximum likelihood estimation of a generalized linear mixed model. They treat the latent random effects in the model as missing data and develop the influence analysis on the basis of a Q‐function which is associated with the conditional expectation of the complete‐data log‐likelihood function in the EM algorithm. They propose a procedure to detect influential observations in six model perturbation schemes. They also illustrate their methodology in a hypothetical situation and in two real cases.     

外电场中氢原子任意激发态能级的求解

本文采用简并微扰理论求解了外电场中氢原子任意激发态的能级，给出了任意激发态能级和主量子数n及磁量子数m的关系以及相应的微扰波函数。     

带引导场圆极化自由电子激光器的电子稳态轨迹

本文用扰动方法,逐级求解了带导引场圆极化自由电子激光器的电子非线性稳态运动方程。计算表明,在一级近似下,文中给出的电子稳态轨迹包含了文献[1]的结果,并且具有比[1]的结果更合理的物理意义。