Inference and optimal design of multiple constant-stress testing for generalized half-normal distribution under type-II progressive censoring

The generalized half-normal (GHN) distribution and progressive type-II censoring are considered in this article for studying some statistical inferences of constant-stress accelerated life testing. The EM algorithm is considered to calculate the maximum likelihood estimates. Fisher information matrix is formed depending on the missing information law and it is utilized for structuring the asymptomatic confidence intervals. Further, interval estimation is discussed through bootstrap intervals. The Tierney and Kadane method, importance sampling procedure and Metropolis-Hastings algorithm are utilized to compute Bayesian estimates. Furthermore, predictive estimates for censored data and the related prediction intervals are obtained. We consider three optimality criteria to find out the optimal stress level. A real data set is used to illustrate the importance of GHN distribution as an alternative lifetime model for well-known distributions. Finally, a simulation study is provided with discussion.     

Random effects regression mixtures for analyzing infant habituation

Random effects regression mixture models are a way to classify longitudinal data (or trajectories) having possibly varying lengths. The mixture structure of the traditional random effects regression mixture model arises through the distribution of the random regression coefficients, which is assumed to be a mixture of multivariate normals. An extension of this standard model is presented that accounts for various levels of heterogeneity among the trajectories, depending on their assumed error structure. A standard likelihood ratio test is presented for testing this error structure assumption. Full details of an expectation-conditional maximization algorithm for maximum likelihood estimation are also presented. This model is used to analyze data from an infant habituation experiment, where it is desirable to assess whether infants comprise different populations in terms of their habituation time.     

消费者网评使用习惯对网评效应的影响研究---网评奖励效果的中介和调节效应探索*

本文以网购旅游者为调查对象，分析其网评使用习惯及其在网评奖励效果作用下对网络评论效用发挥的影响。通过问卷调查和数据分析发现，网评使用习惯和网评奖励效果都对网评效应具有显著正向影响，但网评使用习惯的影响力大于网评奖励效果。同时，网评奖励效果对网评使用习惯与网评效应间的关系具有显著负向调节效应，它削弱了消费者网评使用习惯对网评效应的影响；但消费者的网评使用习惯不通过网站的网评奖励效果对网评效应发挥作用，网评奖励效果不具有中介作用。可见，在网评使用习惯、网评奖励效果和网评效应三者关系中，网评使用习惯是个独立的影响因素，它并不通过网评奖励效果发挥作用，并且由于网评奖励效果的加入，反而削弱了网评使用习惯对网评效应的影响。研究结论表明，对于网评奖励政策的效果，需要客观辩证地看待，不应一味夸大。     

基于TMS320C40的FFT算法改进及应用

讨论了充分利用C4 0的硬件并行结构进行实数FFT的并行算法 ,并在地震勘探信号处理中得以应用     

Non-ignorable missing covariate data in survival analysis: a case-study of an International Breast Cancer Study Group trial

Summary.  Non-ignorable missing data, a serious problem in both clinical trials and observational studies, can lead to biased inferences. Quality-of-life measures have become increasingly popular in clinical trials. However, these measures are often incompletely observed, and investigators may suspect that missing quality-of-life data are likely to be non-ignorable. Although several recent references have addressed missing covariates in survival analysis, they all required the assumption that missingness is at random or that all covariates are discrete. We present a method for estimating the parameters in the Cox proportional hazards model when missing covariates may be non-ignorable and continuous or discrete. Our method is useful in reducing the bias and improving efficiency in the presence of missing data. The methodology clearly specifies assumptions about the missing data mechanism and, through sensitivity analysis, helps investigators to understand the potential effect of missing data on study results.     

网上的西欧政党

20世纪90年代以后，互联网在全球迅速普及，对西欧政党产生了重要的影响。为适应自身发展的需要，获得更多选民的支持，西欧各国政党纷纷上网。他们竞相在网上开展各项活动。主要包括提供信息、网上竞选、政治参与和组织建设，并取得了一定的成效。但是，受到数字鸿沟、政党实力、政党利益、党员和选民态度的影响，西欧政党的网上潜能没有得到充分地发挥，政党的网上效力有待于进一步提高。     

网上购物风险来源、类型及其影响因素研究

从风险来源的角度深入地研究了网上购物感知风险类型与其相对强度,对于企业从风险来源上降低和管理网上风险具有重要的指导意义.文章发现消费者在网上购物时,风险主要来源于产品本身、远距离交易、互联网这种交易模式和网站这四个方面.研究结果揭示了,当消费者在网上购物的时候,所感受到最强烈的三个风险分别是,来自于产品本身的产品绩效风险、来自于远距离交易的服务风险和来自于网站的来源风险.最后,研究还发现,消费者网上购物感知风险的强弱与消费者的年龄、网络购物频率之间存在较强的相关性,年龄越大,感知风险越强;网上购物频率越高,感知风险越低.     

基于RSA的电子商务信息加密技术研究

21世纪是网络信息时代,电子商务的迅猛发展和普及打破了人们传统的经营和消费理念,网上消费已成为一种新的消费形式,但随之而来的便是电子商务赖以生存和发展的安全问题。文章主要通过对电子商务安全隐患的分析,论证了数据加密技术在电子商务安全中的作用,重点探讨了RSA公钥加密算法,并通过实例对其加密原理、计算复杂性等安全性问题作了详尽的分析和阐述。     

A semi-parametric approach to robust parameter design

Parameter design or robust parameter design (RPD) is an engineering methodology intended as a cost-effective approach for improving the quality of products and processes. The goal of parameter design is to choose the levels of the control variables that optimize a defined quality characteristic. An essential component of RPD involves the assumption of well estimated models for the process mean and variance. Traditionally, the modeling of the mean and variance has been done parametrically. It is often the case, particularly when modeling the variance, that nonparametric techniques are more appropriate due to the nature of the curvature in the underlying function. Most response surface experiments involve sparse data. In sparse data situations with unusual curvature in the underlying function, nonparametric techniques often result in estimates with problematic variation whereas their parametric counterparts may result in estimates with problematic bias. We propose the use of semi-parametric modeling within the robust design setting, combining parametric and nonparametric functions to improve the quality of both mean and variance model estimation. The proposed method will be illustrated with an example and simulations.     

Estimating household structure in ancient China by using historical data: a latent class analysis of partially missing patterns

Summary.  Social data often contain missing information. The problem is inevitably severe when analysing historical data. Conventionally, researchers analyse complete records only. Listwise deletion not only reduces the effective sample size but also may result in biased estimation, depending on the missingness mechanism. We analyse household types by using population registers from ancient China (618–907 AD) by comparing a simple classification, a latent class model of the complete data and a latent class model of the complete and partially missing data assuming four types of ignorable and non-ignorable missingness mechanisms. The findings show that either a frequency classification or a latent class analysis using the complete records only yielded biased estimates and incorrect conclusions in the presence of partially missing data of a non-ignorable mechanism. Although simply assuming ignorable or non-ignorable missing data produced consistently similarly higher estimates of the proportion of complex households, a specification of the relationship between the latent variable and the degree of missingness by a row effect uniform association model helped to capture the missingness mechanism better and improved the model fit.