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.     

Modifications of sequential designs in bioequivalence trials

Bioequivalence (BE) studies are designed to show that two formulations of one drug are equivalent and they play an important role in drug development. When in a design stage, it is possible that there is a high degree of uncertainty on variability of the formulations and the actual performance of the test versus reference formulation. Therefore, an interim look may be desirable to stop the study if there is no chance of claiming BE at the end (futility), or claim BE if evidence is sufficient (efficacy), or adjust the sample size. Sequential design approaches specially for BE studies have been proposed previously in publications. We applied modification to the existing methods focusing on simplified multiplicity adjustment and futility stopping. We name our method modified sequential design for BE studies (MSDBE). Simulation results demonstrate comparable performance between MSDBE and the original published methods while MSDBE offers more transparency and better applicability. The R package MSDBE is available at https://sites.google.com/site/modsdbe/ . Copyright © 2015 John Wiley & Sons, Ltd.     

Analyzing multiple vector autoregressions through matrix-variate normal distribution with two covariance matrices

Abstract

This article proposes a new approach to analyze multiple vector autoregressive (VAR) models that render us a newly constructed matrix autoregressive (MtAR) model based on a matrix-variate normal distribution with two covariance matrices. The MtAR is a generalization of VAR models where the two covariance matrices allow the extension of MtAR to a structural MtAR analysis. The proposed MtAR can also incorporate different lag orders across VAR systems that provide more flexibility to the model. The estimation results from a simulation study and an empirical study on macroeconomic application show favorable performance of our proposed models and method.     

Restricted minimax estimation in semiparametric linear models

Abstract

In this article we develop the minimax estimation approach of general linear models to the semiparametric linear models when the parameters are simultaneously constrained by an ellipsoid and linear restrictions. Combining sample information and prior constraints the minimax estimator is obtained and compared with partially least square estimator by theoretical and simulation methods.     

Product partition latent variable model for multiple change-point detection in multivariate data

The product partition model (PPM) is a well-established efficient statistical method for detecting multiple change points in time-evolving univariate data. In this article, we refine the PPM for the purpose of detecting multiple change points in correlated multivariate time-evolving data. Our model detects distributional changes in both the mean and covariance structures of multivariate Gaussian data by exploiting a smaller dimensional representation of correlated multiple time series. The utility of the proposed method is demonstrated through experiments on simulated and real datasets.     

一种新型的GSM接收机的设计与实现

依据GSM协议,结合GSM数字移动通信系统信号传输的特点,给出了基于GSM系统的带软输出结构的MLSE接收机的设计方案,阐述了MLSE接收机的实现算法。实际环境的试验结果表明:工程实现的MLSE接收机具有良好的工作性能和一定的实际应用价值。     

冲激噪声环境中LFM信号的特征参数估计

SαS稳定分布是一类非常重要的非高斯随机分布,具有这类分布的噪声称为冲激噪声。在冲激噪声情况下,α阶以上的矩均不存在,导致基于二阶矩的高斯模型算法性能下降,甚至不能正常工作。该文提出了一种在冲激噪声环境下线性调频信号特征参数估计的算法,通过分析冲激噪声的具体特点,给出了修正的低阶矩模糊函数,并结合Radon变换估计了冲激噪声环境下LFM信号的参数。该算法既可应用于冲激噪声下,又可应用于高斯噪声环境,故具有较好的鲁棒性。最后用计算机仿真验证了该算法的有效性。     

关于元评估

本文阐述了元评估工作的重要意义 ,并对元评估活动所依据的原则 (客观性、整体性　全面性、综合性 )、应遵循的程序和采用的方法 (元研究方法、系统方法、评价方法、模型方法 )作了系统论述。元评估活动将对提高各级领导决策的科学性和自觉性 ,推动社会各个领域乃至整个社会与自然界协调而持续的发展作出宝贵的贡献