A new aging concept derived from the increasing convex ordering

In this paper, a comparison between the life distribution of a new unit with that of a used unit in the increasing convex order is made leading to a new class of life distributions which we call “new better than used in convex ordering of second order”. This class includes as subclasses the NBU and the NBUC and is a subclass of the NBUCA class. Preservation properties under convolution, random maxima, mixing and formation of coherent structures are established. Stochastic comparisons of the excess lifetime when the inter-arrival times belong to the NBUC(2) class are developed. Some applications of Poisson shock models and a test of exponentiality against NBUC(2) alternative are presented.     

Inferences for a Partially Varying Coefficient Model With Endogenous Regressors

In this article, we propose a new class of semiparametric instrumental variable models with partially varying coefficients, in which the structural function has a partially linear form and the impact of endogenous structural variables can vary over different levels of some exogenous variables. We propose a three-step estimation procedure to estimate both functional and constant coefficients. The consistency and asymptotic normality of these proposed estimators are established. Moreover, a generalized F-test is developed to test whether the functional coefficients are of particular parametric forms with some underlying economic intuitions, and furthermore, the limiting distribution of the proposed generalized F-test statistic under the null hypothesis is established. Finally, we illustrate the finite sample performance of our approach with simulations and two real data examples in economics.     

Efficient Bayesian inference in generalized inverse gamma processes for stochastic volatility

This paper develops a novel and efficient algorithm for Bayesian inference in inverse Gamma stochastic volatility models. It is shown that by conditioning on auxiliary variables, it is possible to sample all the volatilities jointly directly from their posterior conditional density, using simple and easy to draw from distributions. Furthermore, this paper develops a generalized inverse gamma process with more flexible tails in the distribution of volatilities, which still allows for simple and efficient calculations. Using several macroeconomic and financial datasets, it is shown that the inverse gamma and generalized inverse gamma processes can greatly outperform the commonly used log normal volatility processes with Student’s t errors or jumps in the mean equation.     

On approximations via convolution-defined mixture models

An often-cited fact regarding mixing or mixture distributions is that their density functions are able to approximate the density function of any unknown distribution to arbitrary degrees of accuracy, provided that the mixing or mixture distribution is sufficiently complex. This fact is often not made concrete. We investigate and review theorems that provide approximation bounds for mixing distributions. Connections between the approximation bounds of mixing distributions and estimation bounds for the maximum likelihood estimator of finite mixtures of location-scale distributions are reviewed.     

Cytometry inference through adaptive atomic deconvolution

In this paper, we consider a statistical estimation problem known as atomic deconvolution. Introduced in reliability, this model has a direct application when considering biological data produced by flow cytometers. From a statistical point of view, we aim at inferring the percentage of cells expressing the selected molecule and the probability distribution function associated with its fluorescence emission. We propose here an adaptive estimation procedure based on a previous deconvolution procedure introduced by Es, Gugushvili, and Spreij [(2008), ‘Deconvolution for an atomic distribution’, Electronic Journal of Statistics, 2, 265–297] and Gugushvili, Es, and Spreij [(2011), ‘Deconvolution for an atomic distribution: rates of convergence’, Journal of Nonparametric Statistics, 23, 1003–1029]. For both estimating the mixing parameter and the mixing density automatically, we use the Lepskii method based on the optimal choice of a bandwidth using a bias-variance decomposition. We then derive some convergence rates that are shown to be minimax optimal (up to some log terms) in Sobolev classes. Finally, we apply our algorithm on the simulated and real biological data.     

指示代词“是”的特质及其发展

作为指示代词,"是"最初指代的是时间,相当于"这时",后泛化为一个确指代词。正因为"是"的确指性,它在先秦时期有一系列的特殊用法。并最终发展成为系词。当用于判断句的主谓之间复指主语的时候,"是"逐步语法化为近乎强调确认的标记。当判断句中必须出现"是",并且"是"的出现不带有任何强调的意味的时候,它就发展成为系词。     

中国生育率研究方法:30年回眸

改革开放30年是我国人口学及人口研究迅速成长的30年。生育率研究方法从一个侧面反映出改革开放以来我国人口学研究方法的发展。中国的生育率研究方法既有从西方引进的各种生育率指标和模型,也有中国学者为了适应自己的需要而改进和创建的方法和模型。笔者从生育率度量指标和生育率模型两个方面总结我国生育率研究方法的应用及变化。对在中国生育率研究中所使用的主要方法进行回顾并对这些方法对我国生育率研究的贡献进行总结。     

上海市人口老龄化：现状、影响及对策思考

人口老龄化已成为当今世界各国经济发展过程面临的共同性话题,就上海而言．未来上海市人口老龄化带来的影响已逐渐凸显,人口老龄化带来的养老金匮乏,劳动力不足以及社区为老服务发展滞后的矛盾已开始成为制约上海市经济发展的瓶颈,老龄化带来的影响将在未来很长一段时间内长期存在．缓解人口老龄化带来的影响以及寻求可行性办法解决老龄化人口的赡养问题已成为政府和社会必须要解决的问题。     

一般本科院校人才培养目标与培养模式研究

文章通过对高等学校分类、定位的介绍，提出了一般本科院校的人才培养目标应根据社会的需求和学生的差异而多元化，即以应用型、复合型为主体，以研究型为激励，以技能型为补充。按照强化基础、拓宽专业、突出能力、注重创新、提高素质、全面发展的模式实施人才培养。     

脑-机接口中新的脑电数据分类方法

根据自发脑电的特点,将HMM-AR模型算法运用到脑电状态的分类中,证明它是一种非常有用的分析脑-机接口方法。将Laplacian filter、ICA和HMM-AR方法相结合,用想象左右手运动的BCI数据进行识别,得到了很好的分类结果,有效地区分脑电中运动与非运动两种状态。该算法能够在运动开始后1 s内检验到脑电信号的变化,从而证明了该算法在BCI的实用性,达到了良好的识别效果。