Storage Life Prediction for a High-Performance Capacitor Using Multi-Phase Wiener Degradation Model

Capacitance is a critical performance characteristic of high-voltage-pulse capacitor which is used to store and discharge electrical energy rapidly. The capacitors usually are stored for a long period of time before put into use. Experimental result and engineering experience indicate that the capacitance increases with storage time and will eventually exceed the failure threshold, which means that the capacitor may fail during storage. This is a typical mode of degradation failure for long storage products. Further, the capacitance degradation path can be extrapolated in several stages based on the shifting characteristics. That is, the capacitance increases slowly or fluctuates in the initial storage stage that lasts about three months. Then it increases sharply in the middle stage which lasts about four months. After the two stages, the capacitor enters into the third stage in which capacitance increases constantly. This degradation phenomenon motivates us to study the storage life prediction method based on multi-phase degradation path model. The storage performance degradation mechanism of high-voltage-pulse capacitor was investigated, which provides the physical basis for multi-phase Wiener degradation model. Identification procedure for the transition points in the degradation path was proposed using maximum likelihood principle (MLP). The result of Kruskal-Wallis test which is the method to test whether two populations are consistent or not in statistics showed that the transition points are statistically effective. Other parameters in the multi-phase degradation model are estimated with maximum likelihood estimation (MLE) after the transition points have been specified. The multi-phase Inverse Gaussian (IG) distribution for storage life was deduced for the capacitor, and the point and interval estimation procedure for reliable storage life are constructed with bootstrap method. The efficiency and effectiveness of the proposed multi-phase degradation model is compared with storage life prediction under single-phase condition.     

Approximate Bayesian Computation for Exponential Random Graph Models for Large Social Networks

We consider the issue of sampling from the posterior distribution of exponential random graph (ERG) models and other statistical models with intractable normalizing constants. Existing methods based on exact sampling are either infeasible or require very long computing time. We study a class of approximate Markov chain Monte Carlo (MCMC) sampling schemes that deal with this issue. We also develop a new Metropolis–Hastings kernel to sample sparse large networks from ERG models. We illustrate the proposed methods on several examples.     

Penalized inverse probability weighted estimators for weighted rank regression with missing covariates

Abstract

In this article, we study the variable selection and estimation for linear regression models with missing covariates. The proposed estimation method is almost as efficient as the popular least-squares-based estimation method for normal random errors and empirically shown to be much more efficient and robust with respect to heavy tailed errors or outliers in the responses and covariates. To achieve sparsity, a variable selection procedure based on SCAD is proposed to conduct estimation and variable selection simultaneously. The procedure is shown to possess the oracle property. To deal with the covariates missing, we consider the inverse probability weighted estimators for the linear model when the selection probability is known or unknown. It is shown that the estimator by using estimated selection probability has a smaller asymptotic variance than that with true selection probability, thus is more efficient. Therefore, the important Horvitz-Thompson property is verified for penalized rank estimator with the covariates missing in the linear model. Some numerical examples are provided to demonstrate the performance of the estimators.     

大股东控制影响公司现金持有量的实证分析

 本文结合我国上市公司所面临的内部治理机制特点及外部治理环境,从大股东行为的视角对公司现金持有量的影响因素进行实证分析。在加入年度控制变量、行业控制变量和公司财务特征控制变量后,对三项反映大股东行为特征的因素,即大股东持股比例、大股东属性和制度环境因素对公司现金持有量的影响进行了检验。结果表明,第一大股东持股比例较高的公司持有较多的现金,大股东的高持股比例是其占用企业资金侵占中小股东利益的基础。与国有企业相比,民营企业因遭受较强的外部融资约束而持有较高水平的现金。此外,制度环境的发展程度与公司现金持有水平正相关,表明制度环境较好地区的公司持有较多的现金。     

《西安交通大学学报（社会科学版）》载文学术影响分析

根据文献计量学原理,对<西安交通大学学报(社会科学版)>刊载的学术论文进行统计分析,探究其在社会科学的研究中所关注的热点,并分析热点随时间的变化趋势.同时,对学术论文的作者、机构的分布,引文数量、类型,被引频次进行统计研究,检验了期刊的目标定位.     

中国制造业集聚：度量与显著性检验 ——基于集聚测量新方法

 根据集聚分布的自身特点,本文提出了可以进行显著性检验的普适性产业集聚指数,并对我国28制造业1994年的860个样本和2004年800个样本进行测量和检验,我们的计算结果表明,中国制造业的集聚强度总体上呈上升趋势,同时具有集聚和扩散双重现象。     

生命伦理问题的国际软法规制

科技无国界,生命科技发展及其医学应用引发的诸多伦理难题跨越国界,日渐成为全球性的问题,亟待世界各国共同应对。以联合国教科文组织为代表的一些国际组织在过去的三十年来采用"宣言"(软法)而不是"条约"(硬法)的方式对生命伦理问题有所规制,创制了解决生命伦理问题的诸多国际软法。我国应重视生命科技国际软法规制之形成,发挥应有的主动权和话语权,积极成为国际社会的引领者。     

社交电商情境下消费者预期价值研究——基于说服抵抗的中介作用及内容煽动性的调节作用

社交电商的发展使得网红带货成为时下最流行的流量变现方式,商家利用社交媒体投放产品广告,借助网红博主的影响力为产品打开市场并建立口碑。基于说服抵抗理论和信息操纵理论,通过两个实验研究社交电商背景下网红博主与产品之间的契合程度对于消费者对产品预期价值的影响,以及该过程中说服抵抗的中介作用和博文内容煽动强度的调节作用。结果表明：与博主-产品契合度低的博文推广内容相比,博主-产品契合度高的博文推广内容能使消费者对产品产生更高的价值预期,且此过程以说服抵抗为中介。同时,内容煽动强度发挥调节作用,当煽动强度存在强弱差异时,不同博主-产品契合度的产品推广博文对消费者预期价值的影响差异逐渐加大。     

金融知识差异与我国家庭财富积累

通过应用中国城市家庭消费金融调研数据,本文系统的实证检验了改进后的金融知识指标、经济学专业、金融学专业以及家庭理财规划等因素对家庭各项财富积累的影响,并通过使用合适的工具变量进行两阶段估计,验证了这种正向影响的稳健性。此外,实证结果中家庭理财规划以及理财规划时间跨度的正向边际效应表明,理财规划可能是金融知识差异影响家庭财富积累的重要机制。研究结论有利于更准确地理解居民金融知识差异对于家庭福利,特别是退休福利的影响。