一类地下水水质污染问题广义解的唯一性及对初值和自由项的连续依赖性

本文讨论了一类地下水水质污染问题,其数学模型为一组非线性双曲—抛物耦合方程组,并证明了在相应初边值条件下广义解的唯一性及对初值和自由项的连续依赖性．     

关于极大η-单调映象的完全广义Fuzzy隐拟变分包含

本文介绍了一类关于极大,η-单调映象的完全广义拟变分包含问题,利用预解算子技巧研究了这类变分包含解的迭代算逼近,证明了解的存在性以及由算法生成的迭代序列的收敛性。     

非单调二元非线性算子方程组的迭代求解

利用锥与半序理论和混合单调算子理论 ,讨论 Banach空间中非单调二元非线性算子方程组解的存在性与唯一性 ,并给出收敛于方程组解的迭代序列和误差估计 ,改进和推广了混合单调算子方程和一元算子方程的某些相应结果 .     

旱灾对粮食生产影响的非线性动态效应评估

自然灾害严重威胁粮食安全,因此科学地理解和认识自然灾害与粮食产量之间的关系至为重要。基于时变Copula函数方法的实证研究结果表明,水稻、小麦、玉米在低产时和旱灾的尾部相关系数分别为0.52、0.15、0.58,但是水稻、玉米在高产时和旱灾比率的尾部相关系数近似为0,小麦在高产时和旱灾的尾部相关系数仅为0？06,这意味着粮食单产与旱灾的关联性表现出非对称性。旱灾对粮食产量影响的时变性也因不同作物而异。对于水稻而言,随着技术水平的提高,旱灾与水稻单产的关联性存在显著的下降趋势;但是,旱灾与小麦、玉米单产的关联性并没有表现出随时间推移而下降的趋势。     

Statistical diagnostics for nonlinear regression models based on scale mixtures of skew-normal distributions

The purpose of this paper is to develop diagnostics analysis for nonlinear regression models (NLMs) under scale mixtures of skew-normal (SMSN) distributions introduced by Garay et al. [Nonlinear regression models based on SMSN distributions. J. Korean Statist. Soc. 2011;40:115–124]. This novel class of models provides a useful generalization of the symmetrical NLM [Vanegas LH, Cysneiros FJA. Assessment of diagnostic procedures in symmetrical nonlinear regression models. Comput. Statist. Data Anal. 2010;54:1002–1016] since the random terms distributions cover both symmetric as well as asymmetric and heavy-tailed distributions such as the skew-t, skew-slash, skew-contaminated normal distributions, among others. Motivated by the results given in Garay et al. [Nonlinear regression models based on SMSN distributions. J. Korean Statist. Soc. 2011;40:115–124], we presented a score test for testing the homogeneity of the scale parameter and its properties are investigated through Monte Carlo simulations studies. Furthermore, local influence measures and the one-step approximations of the estimates in the case-deletion model are obtained. The newly developed procedures are illustrated considering a real data set.     

Structural Nonparametric Cointegrating Regression

Nonparametric estimation of a structural cointegrating regression model is studied. As in the standard linear cointegrating regression model, the regressor and the dependent variable are jointly dependent and contemporaneously correlated. In nonparametric estimation problems, joint dependence is known to be a major complication that affects identification, induces bias in conventional kernel estimates, and frequently leads to ill‐posed inverse problems. In functional cointegrating regressions where the regressor is an integrated or near‐integrated time series, it is shown here that inverse and ill‐posed inverse problems do not arise. Instead, simple nonparametric kernel estimation of a structural nonparametric cointegrating regression is consistent and the limit distribution theory is mixed normal, giving straightforward asymptotics that are useable in practical work. It is further shown that use of augmented regression, as is common in linear cointegration modeling to address endogeneity, does not lead to bias reduction in nonparametric regression, but there is an asymptotic gain in variance reduction. The results provide a convenient basis for inference in structural nonparametric regression with nonstationary time series when there is a single integrated or near‐integrated regressor. The methods may be applied to a range of empirical models where functional estimation of cointegrating relations is required.     

Bounds on Parameters in Panel Dynamic Discrete Choice Models

Identification of dynamic nonlinear panel data models is an important and delicate problem in econometrics. In this paper we provide insights that shed light on the identification of parameters of some commonly used models. Using these insights, we are able to show through simple calculations that point identification often fails in these models. On the other hand, these calculations also suggest that the model restricts the parameter to lie in a region that is very small in many cases, and the failure of point identification may, therefore, be of little practical importance in those cases. Although the emphasis is on identification, our techniques are constructive in that they can easily form the basis for consistent estimates of the identified sets.     

Identification and Estimation of Regression Models with Misclassification

This paper studies the problem of identification and estimation in nonparametric regression models with a misclassified binary regressor where the measurement error may be correlated with the regressors. We show that the regression function is nonparametrically identified in the presence of an additional random variable that is correlated with the unobserved true underlying variable but unrelated to the measurement error. Identification for semiparametric and parametric regression functions follows straightforwardly from the basic identification result. We propose a kernel estimator based on the identification strategy, derive its large sample properties, and discuss alternative estimation procedures. We also propose a test for misclassification in the model based on an exclusion restriction that is straightforward to implement.     

构建省以下政府间合理财政关系问题研究——兼论“省管县”体制向常态化发展的可能性及路径

对于“省管县”财政体制改革的认识,不能仅定位于压缩财政层级或县域政区财政脱困的权宜之计,必须将其提升至地方行政管理体制变革的高度。中国的财政管理体制改革必须在行政管理体制改革的大背景中找到自己的位置。本文认为,应逐步通过解除市领导县的管理体制,重新整合县域行政辖区,以及必要的省级行政区划的调整,在形成了一个相对合理地地方行政管理层级架构之后,方能营造出构建相对合理的地方政府间财政关系的可能性,进而使得“省管县”财政管理体制成为一种常态化的模式。