Deconvolution with supersmooth distributions

Nonparametric deconvolution problems require one to recover an unknown density when the data are contaminated with errors. Optimal global rates of convergence are found under the weighted L_p-loss (1 ≤ p ≤ ∞). It appears that the optimal rates of convergence are extremely low for supersmooth error distributions. To resolve this difficulty, we examine how high the noise level can be for deconvolution to be feasible, and for the deconvolution estimate to be as good as the ordinary density estimate. It is shown that if the noise level is not too high, nonparametric Gaussian deconvolution can still be practical. Several simulation studies are also presented.     

"田野中的宗教"国际学术研讨会简述

2006年2月18至19日,“田野中的宗教”国际学术研讨会在中山大学人类学系召开。本次大会由中山大学人类学系王建新教授、刘昭瑞教授精心组织,来自日本爱知大学、日本神户外国语大学、日本冲绳大学、香港中文大学、中国社会科学院世界宗教研究所、中国社会科学院民族学与人类学研究所、《民族研究》编辑部、《思想战线》编辑部、厦门大学、兰州大学、中央民族大学、陕西师范大学、云南大学、西南民族大学、西北民族大学、广东省民族研究所、中山大学等高校和科研机构的专家、学者36人参加了本次会议。在大会开幕式上,中山大学人类学系系主任周…     

我国地方政府信用档案建设研究

政府信用是当前社会生活和学术研究中的热门议题.政府信用档案是一个庞大而复杂的系统工程,是监督政府、评价政府、治理政府失信和激励政府守信的物质基础.在信息化环境下,地方政府信用档案是可以独立于传统档案而存在的数字化档案体系.系统论的分析表明政府信用档案是由开发应用子系统、信息技术子系统和管理保障子系统组成.档案开发应用子系统是一个目标应用体系,由档案馆藏、监督管理、信誉评价和科学研究等元素构成,信息技术子系统是一个信息处理体系,由数据归集、数据交换、数据比对和数据储存等元素构成;管理保障子系统是一个运行保障体系,由法律保障、组织保障、制度保障、标准保障、资金保障、人才保障等元素构成.     

PES-C,(PP/Bis-T)PES的次级松驰与转变

描述了酚酞型聚芳醚砜(PES-C)的次级松驰和转变与其结构的关系.TBA和动态力学实验结果表明:PES-C及其共聚物(PP/Bis-T)PES分别在0～50℃和-100℃附近呈现出两个次级松驰峰;双酚T单元的引入导致了PES-C在0～50℃之间的次级松驰位置改变.证实了Cardo侧基的存在对-100℃的次级松驰有较大的贡献.     

多方发挥物理实验教学的职能作用

当前我国医学教育中物理实验教学存在弱点:对物理教育不重视,实验条件差,仪器、设备陈旧,实验选项过时,实验课时少.针对存在的问题及现状,在现有的条件之内,我们提出了物理实验课的改进措施:认真做好实验准备,发挥教师的主导作用,督查学生亲自动手,利用现有仪器设备,不断改善实验条件,重视教学情况的反馈信息.     

Statistical inference for partially time-varying coefficient errors-in-variables models

This paper studies the partially time-varying coefficient models where some covariates are measured with additive errors. In order to overcome the bias of the usual profile least squares estimation when measurement errors are ignored, we propose a modified profile least squares estimator of the regression parameter and construct estimators of the nonlinear coefficient function and error variance. The proposed three estimators are proved to be asymptotically normal under mild conditions. In addition, we introduce the profile likelihood ratio test and then demonstrate that it follows an asymptotically χ²${\chi }^2$ distribution under the null hypothesis. Finite sample behavior of the estimators is investigated via simulations too.     

Composite quantile regression for varying-coefficient single-index models

ABSTRACT

The varying-coefficient single-index model (VCSIM) is a very general and flexible tool for exploring the relationship between a response variable and a set of predictors. Popular special cases include single-index models and varying-coefficient models. In order to estimate the index-coefficient and the non parametric varying-coefficients in the VCSIM, we propose a two-stage composite quantile regression estimation procedure, which integrates the local linear smoothing method and the information of quantile regressions at a number of conditional quantiles of the response variable. We establish the asymptotic properties of the proposed estimators for the index-coefficient and varying-coefficients when the error is heterogeneous. When compared with the existing mean-regression-based estimation method, our simulation results indicate that our proposed method has comparable performance for normal error and is more robust for error with outliers or heavy tail. We illustrate our methodologies with a real example.     

First- and Second-order Asymptotics for the Tail Distortion Risk Measure of Extreme Risks

The tail distortion risk measure at level p was first introduced in Zhu and Li (2012 Zhu, L., Li, H. (2012). Tail distortion risk and its asymptotic analysis. Insur. Math. Econ. 51(1):115–121.[Crossref], [Web of Science ®] , [Google Scholar]), where the parameter p ∈ (0, 1) indicates the confidence level. They established first-order asymptotics for this risk measure, as p↑1, for the Fréchet case. In this article, we extend their work by establishing both first-order and second-order asymptotics for the Fréchet, Weibull, and Gumbel cases. Numerical studies are also carried out to examine the accuracy of both asymptotics.     

Semiparametric Smooth Coefficient Stochastic Frontier Model With Panel Data

ABSTRACT

We investigate the semiparametric smooth coefficient stochastic frontier model for panel data in which the distribution of the composite error term is assumed to be of known form but depends on some environmental variables. We propose multi-step estimators for the smooth coefficient functions as well as the parameters of the distribution of the composite error term and obtain their asymptotic properties. The Monte Carlo study demonstrates that the proposed estimators perform well in finite samples. We also consider an application and perform model specification test, construct confidence intervals, and estimate efficiency scores that depend on some environmental variables. The application uses a panel data on 451 large U.S. firms to explore the effects of computerization on productivity. Results show that two popular parametric models used in the stochastic frontier literature are likely to be misspecified. Compared with the parametric estimates, our semiparametric model shows a positive and larger overall effect of computer capital on the productivity. The efficiency levels, however, were not much different among the models. Supplementary materials for this article are available online.     

Automating and evaluating reversible jump MCMC proposal distributions

The reversible jump Markov chain Monte Carlo (MCMC) sampler (Green in Biometrika 82:711–732, 1995) has become an invaluable device for Bayesian practitioners. However, the primary difficulty with the sampler lies with the efficient construction of transitions between competing models of possibly differing dimensionality and interpretation. We propose the use of a marginal density estimator to construct between-model proposal distributions. This provides both a step towards black-box simulation for reversible jump samplers, and a tool to examine the utility of common between-model mapping strategies. We compare the performance of our approach to well established alternatives in both time series and mixture model examples.