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.     

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.     

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。     

脊波导宽频带电磁参数测试技术

针对高损耗介质材料电磁参数的宽频带测试问题,提出了利用脊波导进行测试的新方法,建立了电磁参数测试系统,并采用TRL技术进行了系统校准。该方法仅用3个波段的脊波导即可覆盖2.0～18.0GHz宽频带范围内的电磁参数测试,具有频带宽、体积小、测试精确度高等优点。     

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     

无理数e在电路基本理论中出现的物理意义

对无理数e在电路分析中出现的几个典型例子作了分析讨论，明确指出：由1／e决定的特征常数可以在时间分布上和空间分布上，把一个按指数规律非均匀变化的问题，用一个等效的均匀变化的问题来表示；可以反映初始变化率与最终稳定值之间的联系；可以更深刻地反映某些物理过程的本质特征。     

On the Convergence of the Monte Carlo Maximum Likelihood Method for Latent Variable Models

While much used in practice, latent variable models raise challenging estimation problems due to the intractability of their likelihood. Monte Carlo maximum likelihood (MCML), as proposed by Geyer & Thompson (1992 ), is a simulation-based approach to maximum likelihood approximation applicable to general latent variable models. MCML can be described as an importance sampling method in which the likelihood ratio is approximated by Monte Carlo averages of importance ratios simulated from the complete data model corresponding to an arbitrary value of the unknown parameter. This paper studies the asymptotic (in the number of observations) performance of the MCML method in the case of latent variable models with independent observations. This is in contrast with previous works on the same topic which only considered conditional convergence to the maximum likelihood estimator, for a fixed set of observations. A first important result is that when is fixed, the MCML method can only be consistent if the number of simulations grows exponentially fast with the number of observations. If on the other hand, is obtained from a consistent sequence of estimates of the unknown parameter, then the requirements on the number of simulations are shown to be much weaker.     

非英语专业学生英语学习策略使用频率的研究

本调查以内蒙古工业大学非英语专业二年级的109名学生为样本,采取观察和问卷形式,考察非英语专业学生使用何种学习策略以及学习策略的使用频率.结果表明六种学习策略中,非英语专业学生最常用的策略是补偿策略,最不常用的策略是记忆策略.最后,作者指出本文的教学实践意义:即学习策略的培训应融于课堂教学中,其应用应体现于课堂教学活动中