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.     

一个汉英机器翻译系统中的语义处理模型

简略阐述了三种语义分析理论 -配价语法 ,格语法 ,论旨理论后 ,重点介绍了机器翻译系统所采用的语义模型     

刑事诉讼主体的法理分析--以刑事法律关系为基础

犯罪行为引起的刑事法律关系是考察刑事诉讼主体问题的真正法理基础。国家与被告人作为刑事法律关系的主体是刑事诉讼的当事人 ,被害人及其近亲属不是刑事法律关系的主体 ,故不是刑事诉讼当事人 ,只是发动、支持公法性诉讼的公民追诉人。审判权与公诉权的分立 ,本质上是国家将自身作为自己的对象 ,并在自己(裁判者 )与作为对象的自身 (当事人 )之间建立绝对区分 ,体现了作为公共权力的国家的自我反思节制。其他刑事诉讼主体均不是刑事法律关系的主体 ,按照意志关系或者是国家的代表、被告人和被害人的代理人或帮助人 ,或者是不从属于任何一方的诉讼主体。     

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     

论发达地区农村社会阶层结构的变迁——以绍兴县西蜀阜村为例

此文以绍兴县西蜀阜村为例,基于对大量翔实资料的实证分析,阐述和探讨了经济发达的农村社区在农村工业化和城市化过程中,村落社会结构分化的历史进程、决定因素和现实状况,探讨了阶层分化与农村社会经济变迁的关系,对深化沿海发达地区中心镇村现代化进程的认识,有一定的借鉴意义。     

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.     

小议英语无灵句及其翻译

无灵句是英语中一种很重要的句式结构，能否掌握无灵句的特点及其翻译方法直接影响到英语学习者的学习效果。本文主要概括了无灵句的特点及其分类，并且归纳出较为行之有效的翻译方法。     

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.