Local influence in multilevel models

The authors study the local influence of observations in multilevel regression models. To this end, they perturb simultaneously the variances, responses and design matrix. To measure the local change caused by these perturbations, they use generalized Cook statistics for the fixed and random parameter estimates. Closed form local influence measures also allow them to assess the joint influence of various observations. They suggest a simple computation method and illustrate their results using two examples.     

On the preservation of copula structure under truncation

The author characterizes the copula associated with the bivariate survival model of Clayton (1978) as the only absolutely continuous copula that is preserved under bivariate truncation.     

Outlier detection in high-dimensional regression model

An outlier is defined as an observation that is significantly different from the others in its dataset. In high-dimensional regression analysis, datasets often contain a portion of outliers. It is important to identify and eliminate the outliers for fitting a model to a dataset. In this paper, a novel outlier detection method is proposed for high-dimensional regression problems. The leave-one-out idea is utilized to construct a novel outlier detection measure based on distance correlation, and then an outlier detection procedure is proposed. The proposed method enjoys several advantages. First, the outlier detection measure can be simply calculated, and the detection procedure works efficiently even for high-dimensional regression data. Moreover, it can deal with a general regression, which does not require specification of a linear regression model. Finally, simulation studies show that the proposed method behaves well for detecting outliers in high-dimensional regression model and performs better than some other competing methods.     

Diagnostics of multiple group influential observations for logistic regression models

In this paper, two new multiple influential observation detection methods, GCD.GSPR and mCD*, are introduced for logistic regression. The proposed diagnostic measures are compared with the generalized difference in fits (GDFFITS) and the generalized squared difference in beta (GSDFBETA), which are multiple influential diagnostics. The simulation study is conducted with one, two and five independent variable logistic regression models. The performance of the diagnostic measures is examined for a single contaminated independent variable for each model and in the case where all the independent variables are contaminated with certain contamination rates and intensity. In addition, the performance of the diagnostic measures is compared in terms of the correct identification rate and swamping rate via a frequently referred to data set in the literature.     

Robust Methods of Building Regression Models—An Application to thè Housing Sector

This article studies robustification strategies for the linear model in the presence of outliers. The advantages of an internal analysis of the robustness of least squares for a given sample are pointed out. The application of this methodology is illustrated by building an explicit model of the determinants of rental housing values in the Madrid Metropolitan Area.     

LOCAL INFLUENCE IN NONLINEAR REPRODUCTIVE DISPERSION MODELS

Nonlinear reproductive dispersion models (NRDM, Jorgensen 1997) include a wider range of distributions and nonlinear models such as the possibility of correlated errors and nonlinear hypotheses dropping the exponential family assumption. Based on the generalized Cook distance and the conformal normal curvature of Poon & Poon (1999), local influence of minor perturbations on the data set is investigated for NRDM. Two examples are used to illustrate our results.     

中国省际GDP强影响性特征及其形成机制研究

本文采用最新前沿的宏观经济数据诊断和空间面板模型理论和方法,对我国30个省（西藏除外）1999-2011年的省际GDP强影响性特征进行了实证分析,在此基础上,进一步进行B-N分解深入研究了其形成机制。主要结论表明：我国省际GDP不仅空间关联,而且存在着显著的强影响性特征,每一年样本信息对于样本期内全部信息的影响程度和大小存在着差异。同时,我国省际GDP强影响性特征具有极其显著的形成机制,随机性成分是造成强影响性的主要根源,周期成分也产生较为重要的影响。以上结论对于我国省际GDP强影响性特征判断具有十分重要的政策含义,进一步对于我国政府熨平经济波动、实现宏观经济的平稳较快增长的政策制订和完善等具有十分重要的现实借鉴意义。     

Influential observations in transformations: A bayesian approach

This paper presents a bayesian approach to the problem of detecting influential observations when estimating the Box-Cox transformation. The influence of a group I={i₁, …,i_n} of observations is measured by means of the Kullback-Leibler distance between the marginal posterior; distributions for the transformation parameter which are computed, respectively, without and with the cases indexed by I. A measure is proposed and its properties and relationship to other diagnostic methods are studied.     

关于COOK算例的数值解

COOK算例没有理论解,文献中常用的参考解与大量优秀单元的计算解有较明显差别,影响了对单元性能的客观公正评价。为获得更准确、更可靠的计算解,分别用4-40次高次元和Q8、T6、QM6低次元进行逼近,考察算例的有限元渐进收敛解。结果表明,在5位有效数字下该问题的解为vC=23.967,σAmax=0.23687,σBmax=-0.20352。     

线性模型中参数估计的影响分析

研究数据对参数估计的影响是一个重要的问题。找出那些对回归分析影响很大的数据是非常有理论价值和应用价值的。本文主要介绍这方面的基本结果。