Accurate computation of single and product moments of order statistics under progressive censoring

An accurate procedure is proposed to calculate approximate moments of progressive order statistics in the context of statistical inference for lifetime models. The study analyses the performance of power series expansion to approximate the moments for location and scale distributions with high precision and smaller deviations with respect to the exact values. A comparative analysis between exact and approximate methods is shown using some tables and figures. The different approximations are applied in two situations. First, we consider the problem of computing the large sample variance–covariance matrix of maximum likelihood estimators. We also use the approximations to obtain progressively censored sampling plans for log-normal distributed data. These problems illustrate that the presented procedure is highly useful to compute the moments with precision for numerous censoring patterns and, in many cases, is the only valid method because the exact calculation may not be applicable.     

Ordering results for series and parallel systems comprising heterogeneous exponentiated Weibull components

In this paper, we discuss the usual stochastic and reversed hazard rate orders between the series and parallel systems from two sets of independent heterogeneous exponentiated Weibull components. We also obtain the results concerning the convex transform orders between parallel systems and obtain necessary and sufficient conditions under which the dispersive and usual stochastic orders, and the right spread and increasing convex orders between the lifetimes of the two systems are equivalent. Finally, in the multiple-outlier exponentiated Weibull models, based on weak majorization and p-larger orders between the vectors of scale and shape parameters, some characterization results for comparing the lifetimes of parallel and series systems are also established, respectively. The results of this paper can be used in practical situations to find various bounds for the important aging characteristics of these systems.     

Prediction of future failures times based on Type-I hybrid censored samples of random sample sizes

In this article, we consider the prediction of future failure times based on Type-I hybrid censored samples. Point predictors and prediction intervals using different procedures are discussed for a general model. The exponential and Rayleigh distributions are used as illustrative examples to show the most simplified forms of the so obtained predictors as well as prediction intervals. Intensive simulation study and a real life dataset are presented to illustrate our findings and results.     

《民族学刊》2014~2018年影响因子比较分析——基于《中国学术期刊影响因子年报（人文社会科学）》数据

《民族学刊》于2010年6月创刊,相继入选“复印报刊资料重要转载来源期刊”“北大核心期刊”“人文社科核心期刊”和“CSSCI来源期刊”,表明了《民族学刊》在学界和期刊界的学术影响,其期刊的影响因子值得研究和探讨。本文基于《中国学术期刊影响因子年报（人文社会科学）》公布的影响因子、各类计量指标和人文社科类影响因子、被引频次及可被引文献量等数据,对《民族学刊》的相应数据与C95民族学类期刊在期刊影响力指数、影响因子均值及量效指数均值、各类计量指标均值和人文社科类影响因子、被引频次及可被引文献量均值等方面进行统计对比,分析《民族学刊》2014~2018年五年间的影响因子数据在民族学类期刊位置及其学术影响。     

基于遗传算法的进化支持向量机研究

支持向量机是最近发展起来的一种新的通用的机器学习方法 ,其理论基础是统计学习理论 ,支持向量机无论在模式识别还是在函数拟合方面均显示了其优越性 ,并越来越受到国内外研究者的广泛关注 .但是 ,对支持向量机的推广预测能力具有很大影响的核函数和参数C一直没有一个很好的确定方法 ,针对这一问题 ,将遗传算法和支持向量机结合 ,提出了一种自动选择支持向量机参数的方法 ,结果表明 ,这种方法是科学有效的 .     

学校乒乓球业余训练的研究

乒乓球运动需要人才辈出。“塔基”式的人才培养是在众多的基层单位有组织管理的全民健身运动活动中形成和发展起来的。学校开展业余训练工作 ,需要在完成教学工作量的前提下 ,利用课余时间、两个假期以及双休日有目的地培养和发现有潜力的运动员人才。培训过程需要专业性、系统性和长期性 ,这样做有利于少儿运动员身心健康成长 ,有利于基本功形成专业化 ,并且能够活跃校园体育文化活动。同时 ,业余训练能够出成绩 ,能够参加各级各类比赛活动     

改进高校毕业生就业率统计的思考

高校毕业生就业率统计对评价高校办学、把握人才需求、改进人才培养模式、加强就业服务、完善市场配置体系等方面具有积极意义。但当前就业率统计存在指标体系不完善、统计主体单一、统计时点不合理等问题。实现就业率的效用应采取完善统计指标体系、建立多层次统计系统、创新统计方式、实行网络化实名制就业制度、建立监督核查机制等措施,以进一步改进就业率统计。     

简论企业统计核算与会计核算的统一化

统计核算与会计核算 ,经历了同源与分流、渗透与溶合两大阶段。两种核算在对象、方法、目标、原则诸方面存有异同。两种核算统一化的出路在于一体化 ,即 :满足企业内外管理需要 ,建立企业核算数据和信息系统 ,保证企业核算数据一元化和信息整体化。     

安徽省旅游开发空间自相关统计

利用空间自相关分析方法对安徽省17个城市旅游开发的空间关联情况进行数据分析。选取2008年各市人均旅游收入,计算出全局空间自相关指数I及标准化统计量Z值,发现安徽省城市旅游开发在整体上存在着显著的空间自相关。各市的局部空间自相关G统计表明,安徽省人均旅游收入局部集聚的空间结构主要是正的空间联系,属于低-低集聚和高-高集聚类型。     

统计学专业本科生开设“数据挖掘”课程的探讨

"数据挖掘"是一门分析海量数据库的交叉学科,统计学专业开设这门课程十分必要。鉴于数据挖掘内容的复杂多样,本文提出应根据统计学专业本科生的特点制订合理的教学大纲、教学内容和教学方法,以提高学生的学习效率和学习热情。