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.     

实现公有制与市场经济有效结合的四大层次

因所有制关系是生产关系的总和,所有制必须实现在社会生产再生产的过程之中,因此,我们必须从四大层次实现公有制与市场经济的有效结合。一是所有制结构层次。保持公有制主体地位是保证社会主义市场经济体制有效运作的基础,要大力发展多种所有制经济。二是公有制实现形式层次。更新公有制的微观实现形式,更新公有制的宏观实现形式。三是企业制度层次。建立国有资产出资人制度,合理配置股权,强化所有者约束功能,加强治理结构中的权利组织建设,建立权责分明的组织体系。四是社会剩余产品分配层次。按劳分配与按生产要素分配相结合是通过三个层次实现的:微观经济领域的运行层次,微观经济领域中的所有制结构层次,宏观经济运行层次。     

近代中国整合中西文化冲突的价值选择模式及现代启示

中国近代文化变迁的过程 ,在一定意义上表现为中西文化的冲突与整合的过程。为整合中西文化冲突 ,近代中国先后采取了文化传统主义、文化折中主义、文化虚无主义与文化综合创新等价值选择模式。近代文化整合的经验教训启示我们 ,在新时期文化建设问题上 ,必须正确处理文化的一元与多元、整体性与可分性、时代性与民族性的关系 ,坚持走综合创新之路     

论所用权 --与民法所有权的比较分析

所有权是民法 (外 )物权的核心 ,而所用权则是经济法 (内 )物权的核心。创设所用权具有重大的理论意义和实践意义 ,因为这是在财富创造的竞争中赋予人们一种新的权利并使之得到法制的保障 ,这有利于我们坚持“发展才是硬道理”的原则 ,从而把我国的经济建设搞得更好     

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.     

ASSESSING CONGRUENCEAMONG DISTANCE MATRICES: SINGLE-MALT SCOTCH WHISKIES REVISITED

A test of congruence among distance matrices is described. It tests the hypothesis that several matrices, containing different types of variables about the same objects, are congruent with one another, so they can be used jointly in statistical analysis. Raw data tables are turned into similarity or distance matrices prior to testing; they can then be compared to data that naturally come in the form of distance matrices. The proposed test can be seen as a generalization of the Mantel test of matrix correspondence to any number of distance matrices. This paper shows that the new test has the correct rate of Type I error and good power. Power increases as the number of objects and the number of congruent data matrices increase; power is higher when the total number of matrices in the study is smaller. To illustrate the method, the proposed test is used to test the hypothesis that matrices representing different types of organoleptic variables (colour, nose, body, palate and finish) in single‐malt Scotch whiskies are congruent.     

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.