VIF-based adaptive matrix perturbation method for heteroskedasticity-robust covariance estimators in the presence of multicollinearity

In this study, we investigate linear regression having both heteroskedasticity and collinearity problems. We discuss the properties related to the perturbation method. Important observations are summarized as theorems. We then prove the main result that states the heteroskedasticity-robust variances can be improved and that the resulting bias is minimized by using the matrix perturbation method. We analyze a practical example for validation of the method.     

Parametrizing the Kepler exoplanet period-radius distribution with the bivariate normal inverse Gaussian distribution

This paper presents a simple and robust method for obtaining a comprehensive understanding of the joint period and radius distribution in Kepler exoplanets. The proposed method is based on particle swarm optimization and bivariate Normal Inverse Gaussian distribution. Furthermore, in the construction of the probability density function, this study selects planet-host stars with the GK-type. The injecting approach is also employed to solve the survey completeness of sample. The resulting occurrence rate of Earth analogs is 0.025 with a 95% bootstrap confidence interval between 0.023 and 0.032.     

A new algorithm for clustering based on kernel density estimation

In this paper, we present an algorithm for clustering based on univariate kernel density estimation, named ClusterKDE. It consists of an iterative procedure that in each step a new cluster is obtained by minimizing a smooth kernel function. Although in our applications we have used the univariate Gaussian kernel, any smooth kernel function can be used. The proposed algorithm has the advantage of not requiring a priori the number of cluster. Furthermore, the ClusterKDE algorithm is very simple, easy to implement, well-defined and stops in a finite number of steps, namely, it always converges independently of the initial point. We also illustrate our findings by numerical experiments which are obtained when our algorithm is implemented in the software Matlab and applied to practical applications. The results indicate that the ClusterKDE algorithm is competitive and fast when compared with the well-known Clusterdata and K-means algorithms, used by Matlab to clustering data.     

The exponential distribution analog of the Grubbs–Weaver method

Abstract

Grubbs and Weaver (1947 Grubbs, F. E., and C. L. Weaver. 1947. The best unbiased estimate of population standard deviation based on group ranges. Journal of the American Statistical Association 42 (238):224–41. doi: 10.2307/2280652.[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) suggest a minimum-variance unbiased estimator for the population standard deviation of a normal random variable, where a random sample is drawn and a weighted sum of the ranges of subsamples is calculated. The optimal choice involves using as many subsamples of size eight as possible. They verified their results numerically for samples of size up to 100, and conjectured that their “rule of eights” is valid for all sample sizes. Here we examine the analogous problem where the underlying distribution is exponential and find that a “rule of fours” yields optimality and prove the result rigorously.     

Multi-objective optimization in partitioning the healthcare system of Parana State in Brazil

Motivated by a proposal of the local authority for improving the existing healthcare system in the Parana State in Brazil, this article presents an optimization-based model for developing a better system for patients by aggregating various health services offered in the municipalities of Parana into some microregions. The problem is formulated as a multi-objective partitioning of the nodes of an undirected graph (or network) with the municipalities as the nodes and the roads connecting them as the edges of the graph. Maximizing the population homogeneity in the microregions, maximizing the variety of medical procedures offered in the microregions, and minimizing the inter-microregion distances to be traveled by patients are considered as three objective functions of the problem. An integer-coded multi-objective genetic algorithm is adopted as the optimization tool, which yields a significant improvement to the existing healthcare system map of the Parana State. The results obtained may have a strong impact on the healthcare system management in Parana. The model proposed here could be a useful tool to aid the decision-making in health management, as well as for better organization of any healthcare system, including those of other Brazilian States.     

大学生学科文化自信培育的价值逻辑及路径探析 ——以河北工程大学地球科学与工程学院为例

学科文化是学科的灵魂，是学科不断发展创新的精神源泉和内在动力。学科文化自信是文化自信的重要组成部分，培育大学生文化自信应着眼全局，整体推进。学科文化自信与文化自信具有统一性，与核心价值观培育具有同构性，与担当民族复兴大任具有生成性。培育大学生学科文化自信，要深入挖掘和整合学科文化内涵，不断创新和发展培育手段，最终达到浸润和形塑博学自信的新时代大学生的目标。学科文化自信的培育可以达到激发学生学术热情，促进学生和学科全面发展，弘扬社会主义核心价值观，坚定理想信念以及践行使命担当的效用。     

“健康中国”背景下全民健身与全民健康融合研究

结合我国实际，从全民健身和全民健康的科学理念、科学研究、人才培养、体医融合、设施建设、产业发展、“互联网+”运用等措施，实现全民健身和全民健康的深度融合，保障政府工作落地生根，促进全民健康。     

以意识形态安全为核心加强高校国防教育

随着习近平总书记提出总体国家安全观，关于国家安全的研究不断丰富和扩大。文化安全作为国家安全的重要保障，意识形态领域的安全又是文化安全的根本要求，高校学生的思维易受外界影响，需要重点教育引导。高校国防教育活动作为意识形态安全教育的主要形式，对于加强国家文化安全有着重要作用。为了切实加强社会主义意识形态安全教育，为国家发展做好文化安全保障，高校国防教育应该从国家安全的高度指导教学，改革教育的内容和方式。     

微博视阈下高校思想政治教育路径构建

当前,微博成了最受人们热捧的信息传播和交流工具,可以说我们已经进入到了＂微时代＂。微博对乐于接受和追捧新鲜事物的大学生的思想和行为产生了巨大影响,同时也受到了高校思想政治教育者的高度重视。本文拟从微博的传播特征、微博给高校思想政治教育工作带来的机遇和挑战入手,探寻构建＂微时代＂高校思想政治教育的新路径。     

Resource Distribution in Multiple Attacks with Imperfect Detection of the Attack Outcome

This article extends the previous research of consecutive attacks strategy by assuming that an attacker observes the outcome of each attack imperfectly. With given probabilities it may wrongly identify a destroyed target as undestroyed, and wrongly identify an undestroyed target as destroyed. The outcome of each attack is determined by a contest success function that depends on the amount of resources allocated by the defender and the attacker to each attack. The article suggests a probabilistic model of the multiple attacks and analyzes how the target destruction probability and the attacker's relative resource expenditure are impacted by the two probabilities of incorrect observation, the attacker's and defender's resource ratio, the contest intensity, the number of attacks, and the resource distribution across attacks. We analyze how the attacker chooses the number of attacks, the attack stopping rule, and the optimal resource distribution across attacks to maximize its utility.