非自治方程奇摄动问题解的渐近性分析

利用稳定性理论,分析了非线性非自治奇摄动方程εy″=y-ty′-(y′)4,t∈(-1,1)Robin问题边界值对解的渐近性态的影响,得出了边界层存在的位置与渐近解存在的条件.     

奇异常系数线性系统解的存在性及一般解的公式

:研究了一类奇异线性系统初值问题可解的充要条件及通解公式 ,给出了奇异线性系统的化简方法及其简单形式 ,所得结果统一和推广了正常线性系统的相应结果     

Influence analysis on crossover design experiment in bioequivalence studies

Crossover designs are commonly used in bioequivalence studies. However, the results can be affected by some outlying observations, which may lead to the wrong decision on bioequivalence. Therefore, it is essential to investigate the influence of aberrant observations. Chow and Tse in 1990 discussed this issue by considering the methods based on the likelihood distance and estimates distance. Perturbation theory provides a useful tool for the sensitivity analysis on statistical models. Hence, in this paper, we develop the influence functions via the perturbation scheme proposed by Hampel as an alternative approach on the influence analysis for a crossover design experiment. Moreover, the comparisons between the proposed approach and the method proposed by Chow and Tse are investigated. Two real data examples are provided to illustrate the results of these approaches. Our proposed influence functions show excellent performance on the identification of outlier/influential observations and are suitable for use with small sample size crossover designs commonly used in bioequivalence studies. Copyright © 2013 John Wiley & Sons, Ltd.     

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。     

有限奇异酉几何与PBIB设计(Ⅱ)

用有限奇异酉几何中的1维非迷向子空间作处理构作了多个结合类的对称结合方案和相应的一些PBIB设计,并计算了全部参数.     

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。