On multiple constraint skewed models

The Azzalini [A. Azzalini, A class of distributions which includes the normal ones, Scandi. J. Statist. 12 (1985), pp. 171–178.] skew normal model can be viewed as one involving normal components subject to a single linear constraint. As a natural extension of this model, we discuss skewed models involving multiple linear and nonlinear constraints and possibly non-normal components. Particular attention is devoted to a distribution called the extended two-piece normal (ETN) distribution. This model is a two-constraint extension of the two-piece normal model introduced by Kim [H.J. Kim, On a class of two-piece skew normal distributions, Statistics 39(6) (2005), pp. 537–553.]. Likelihood inference for the ETN distribution is developed and illustrated using two data sets.     

On minimaxity of the normal precision matrix estimator of Krishnamoorthy and Gupta

We consider the orthogonally invariant estimation problem of the inverse of the scale matrix of Wishart distribution using Stein's loss (entropy loss). In this problem Krishnamoorthy and Gupta [2] Krishnamoorthy, K. and Gupta, A. K. (1989). Improved minimax estimation of a normal precision matrix. Canad. J. Statist., 17: 91–102. [Crossref], [Web of Science ®] , [Google Scholar] proposed an estimator and showed its good performance in a Monte Carlo simulation. They conjectured their estimator is minimax. Perron [3] Perron, F. (1997). On a conjecture of Krishnamoorthy and Gupta. J. Multivariate Anal., 62: 110–120.  [Google Scholar] proved its minimaxity for p?=?2. In this paper we prove it for p?=?3 by using a new method.     

Analysis of hybrid censored competing risks data

In this paper, we consider the analysis of hybrid censored competing risks data, based on Cox's latent failure time model assumptions. It is assumed that lifetime distributions of latent causes of failure follow Weibull distribution with the same shape parameter, but different scale parameters. Maximum likelihood estimators (MLEs) of the unknown parameters can be obtained by solving a one-dimensional optimization problem, and we propose a fixed-point type algorithm to solve this optimization problem. Approximate MLEs have been proposed based on Taylor series expansion, and they have explicit expressions. Bayesian inference of the unknown parameters are obtained based on the assumption that the shape parameter has a log-concave prior density function, and for the given shape parameter, the scale parameters have Beta–Gamma priors. We propose to use Markov Chain Monte Carlo samples to compute Bayes estimates and also to construct highest posterior density credible intervals. Monte Carlo simulations are performed to investigate the performances of the different estimators, and two data sets have been analysed for illustrative purposes.     

The beta generalized gamma distribution

For the first time, a new five-parameter distribution, called the beta generalized gamma distribution, is introduced and studied. It contains at least 25 special sub-models such as the beta gamma, beta Weibull, beta exponential, generalized gamma (GG), Weibull and gamma distributions and thus could be a better model for analysing positive skewed data. The new density function can be expressed as a linear combination of GG densities. We derive explicit expressions for moments, generating function and other statistical measures. The elements of the expected information matrix are provided. The usefulness of the new model is illustrated by means of a real data set.     

A Comparison of Multiple Outlier Detection Methods for Regression Data

The problem of outliers in statistical data has attracted many researchers for a long time. Consequently, numerous outlier detection methods have been proposed in the statistical literature. However, no consensus has emerged as to which method is uniformly better than the others or which one is recommended for use in practical situations. In this article, we perform an extensive comparative Monte Carlo simulation study to assess the performance of the multiple outlier detection methods that are either recently proposed or frequently cited in the outlier detection literature. Our simulation experiments include a wide variety of realistic and challenging regression scenarios. We give recommendations on which method is superior to others under what conditions.     

Haddon矩阵模型在技巧啦啦队训练损伤防范中的运用

本文应用Haddon矩阵模型理论对技巧啦啦队训练伤害的防范进行研究。试图以新的视角寻找损伤防范的规律,建立新的啦啦队训练赛事精准分析矩阵模型,将弥补技巧啦啦队训练伤害防范中的理论空白。并依据新矩阵模型,研究从运动者、物理环境、社会环境以及媒介四个方面提出减少训练损伤的方法。     

教育考试命题入闱管理模式探析

本文追溯了命题入闱的历史渊源,对新时期教育考试命题采取入闱管理模式的必要性与可行性展开探讨,并针对教育考试命题的组织特征提出了入闱命题"三、二、一"管理模式,分析了该模式的内涵与特色,对教育考试命题组织工作做了初步探索.     

传感器个数及距离对盲分离影响的分析

在使用盲分离技术进行信号处理时,分离高频信号和低频信号对传感器分布的要求是不同的,而传统的方法在处理高低频混合信号时采用了统一的模式,得不到很好的分离效果。为了在采用盲分离技术进行高低频混合信号处理时能得到更好的效果,文章对传感器个数和距离对盲信号分离的影响进行了深入的分析,并提出了一种能有效处理高频和低频混合信号的模型——分离子系统模型。该模型可以根据频段对传感器距离进行调整,分别对高、低频混合信号进行分离。通过实验证明分离效果更好。     

高校学报项目化管理探究——以《辽宁工程技术大学学报》为例

为了进一步提高高校学报出版活动效率,采用项目管理的方法对高校学报出版活动进行研究。分析了高校学报项目化管理的可行性。针对高校学报出版发行活动,制定了一套切实可行的操作计划,运用WBS方法对学报出版项目工作进行层层分解,项目责任矩阵图将所分解的工作落实到有关部门或个人。为确保能够在规定的时间内实现期刊出版项目的目标,编制了学报出版项目的甘特图,利用Project软件对现有人力资源和进度进行调配,使学报出版工期在原来50天的基础上缩短了16天。可见通过运用科学的项目管理工具对高校学报出版发行活动进行管理,使得高校学报管理工作更加规范化、科学化。     

高师音乐教育课的“双选制”利弊

"双选制"作为一种新的教学管理模式,被许多高校所采用,特别是在专业音乐院校的技能技巧课中其优势非常明显.目前许多高师音乐教育专业在技能技巧课中,也采用"双选制",但由于种种原因,这种管理模式反倒成了高师音乐教育"重技术,轻理论","重专业,轻文化"的催化剂.这一现象应该引起我们的注意.