A Generalization of the Univariate Slash by a Scale-Mixtured Exponential Power Distribution

A generalization of the slash distribution is derived using the scale mixture of the exponential power distribution. The newly defined family of distributions provides a rich flexibility on the tail heaviness and yields alternative robust estimators of location and scale in non normal situations. In order to investigate asymptotically the bias properties of the estimators, a simulation study is performed. The performance of the estimators on two well-known real data sets is also illustrated.     

Estimation of Change Point in Generalized Variance Control Chart

In this study, using maximum likelihood estimation, a considerably effective change point model is proposed for the generalized variance control chart in which the required statistics are calculated with its distributional properties. The procedure, when used with generalized variance control charts, would be helpful for practitioners both controlling the multivariate process dispersion and detecting the time of the change in variance-covariance matrix of a process. The procedure starts after the chart issues a signal. Several structural changes for the variance-covariance matrix are considered and the precision and the accuracy of the proposed method is discussed.     

美国单亲家庭研究的理论与方法评述

美国对单亲家庭的研究起步于本世纪初,至今已研究得相当深入.美国对单亲问题的关注,最早是由黑人家庭的问题引发的,对它的研究大致经历了三个发展阶段:20世纪60年代中期以前为其早期,60年代后期至80年代初期为探索与发展时期,80年代中期以来为走向成熟的时期.对单亲家庭的认识也从"病理--解组观点"、"坚强--坚韧观点"走向单亲家庭的结构性变化带来社会资本受损的观点.当前所采取的是实证研究方法.     

Some factors affecting statistical power of approximate tests in the linear mixed model for longitudinal data

Different longitudinal study designs require different statistical analysis methods and different methods of sample size determination. Statistical power analysis is a flexible approach to sample size determination for longitudinal studies. However, different power analyses are required for different statistical tests which arises from the difference between different statistical methods. In this paper, the simulation-based power calculations of F-tests with Containment, Kenward-Roger or Satterthwaite approximation of degrees of freedom are examined for sample size determination in the context of a special case of linear mixed models (LMMs), which is frequently used in the analysis of longitudinal data. Essentially, the roles of some factors, such as variance–covariance structure of random effects [unstructured UN or factor analytic FA0], autocorrelation structure among errors over time [independent IND, first-order autoregressive AR1 or first-order moving average MA1], parameter estimation methods [maximum likelihood ML and restricted maximum likelihood REML] and iterative algorithms [ridge-stabilized Newton-Raphson and Quasi-Newton] on statistical power of approximate F-tests in the LMM are examined together, which has not been considered previously. The greatest factor affecting statistical power is found to be the variance–covariance structure of random effects in the LMM. It appears that the simulation-based analysis in this study gives an interesting insight into statistical power of approximate F-tests for fixed effects in LMMs for longitudinal data.     

论三苏散传的艺术成就

苏洵、苏轼、苏辙的散传创作,代表了宋代传记的最高成就,但是对它的研究却不多。三苏散传的立意各别,不主一格;结构布局,常能突破传统传记的格局,别出心裁;既善于根据传主的个性特征而采用不同的写法,也善于用简洁精炼、富有表现力的语言来刻画人物形象,其散传有自己独特的艺术风貌。     

Organizational wisdom practices and firm product innovation

Review of Managerial Science - The manner in which organizational wisdom-related variables or practices (e.g., reasoning, intuition, virtue, prudence, and aesthetics) are related to firm product...     

杨维桢书法风格的特征和意义

杨维桢的书法风格在中国书法史上别具一格。文章首先阐述了杨维桢书法风格的特征:1.清劲高古,冷峭秀逸;2.狂放不羁,乱头粗服。其次论述了杨维桢书法艺术的成就及影响。     

工业设施选址的景观生态研究

本文通过运用景观生态和生态承载力的观点对传统的工业设施选址问题给予了重新诠释。通过在景观生态层面上异质体的干扰造成的景观破碎化分析、生态承载力层面上的承载能力分析以及传统工业设施选址问题的分析,以整体生态的视角重新对工业设施的选址问题进行了定性和定量化的研究。     

A novel feature selection scheme for high-dimensional data sets: four-Staged Feature Selection

Classification of high-dimensional data set is a big challenge for statistical learning and data mining algorithms. To effectively apply classification methods to high-dimensional data sets, feature selection is an indispensable pre-processing step of learning process. In this study, we consider the problem of constructing an effective feature selection and classification scheme for data set which has a small number of sample size with a large number of features. A novel feature selection approach, named four-Staged Feature Selection, has been proposed to overcome high-dimensional data classification problem by selecting informative features. The proposed method first selects candidate features with number of filtering methods which are based on different metrics, and then it applies semi-wrapper, union and voting stages, respectively, to obtain final feature subsets. Several statistical learning and data mining methods have been carried out to verify the efficiency of the selected features. In order to test the adequacy of the proposed method, 10 different microarray data sets are employed due to their high number of features and small sample size.