When is n large enough? Looking for the right sample size to estimate proportions

The central limit theorem indicates that when the sample size goes to infinite, the sampling distribution of means tends to follow a normal distribution; it is the basis for the most usual confidence interval and sample size formulas. This study analyzes what sample size is large enough to assume that the distribution of the estimator of a proportion follows a Normal distribution. Also, we propose the use of a correction factor in sample size formulas to ensure a confidence level even when the central limit theorem does not apply for these distributions.     

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.     

On MLEs of the parameters of a modified Weibull distribution based on record values

The modified Weibull distribution can be used quite effectively to model complex data from mechanical engineering or survival analysis studies that posses a monotonic or a bathtub-shape hazard rate. In this paper, we study the MLEs of the parameters of a modified Weibull distribution model in the presence of upper kth record values. The existence and uniqueness of the MLEs are proven in this case. Real data analysis is performed for illustrative purposes.     

The laws of large numbers for Pareto-type random variables with infinite means

In this paper, we consider the laws of large numbers for NSD random variables satisfying Pareto-type distributions with infinite means. Based on the Pareto-Zipf distributions, some weak laws of large numbers for weighted sums of NSD random variables are obtained. Meanwhile, we show that a weak law for Pareto-Zipf distributions cannot be extended to a strong law. Furthermore, based on the two tailed Pareto distribution, a strong law of large numbers for weighed NSD random variables is presented. Our results extend the corresponding earlier ones.     

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.     

A volume based approach to establish B-spline based expressions for density functions and its application to progressive hybrid censoring

We propose an approach to determine the distribution of particular linear combinations of hybrid censored order statistics which is based on the calculation of volumes of polytopes. For this purpose, we establish efficient and compact volume formulas in terms of B-splines. Further, we illustrate our approach for ten different progressive hybrid censoring schemes under an exponential assumption.     

需求分布规律变化情况下的报童订货策略

在需求分布规律变化情况下,报童在进行订货决策时会因为错误判断需求分布规律而导致期望库存成本增加。为了解决这一问题,本文集成传统历史需求信息和非传统需求信息以正确地认知需求分布规律,在此基础上决策订货量。假设需求服从均值不同、方差相同的两种类型的正态分布,每一种正态分布的概率已知。利用信号检测理论构建基于历史需求信息与需求分布概率的报童最优订货策略,并与只基于需求分布概率的直觉规则订货策略进行对比。结果表明：只要排除需求分布概率很大或很小两种极端情况,最优订货策略比直觉规则订货策略在控制期望库存成本方面的作用更明显,即利用历史需求信息可以有效修正报童对实际需求分布的检测结果,从而提高实际订货决策的准确性。研究结果对传统历史需求信息和非传统需求信息的集成以及需求信息交换等有一定的管理学启示和应用价值。     

考虑时效与公平性的震后应急物资动态配送优化研究

大规模地震后应急物资的高效保障是应急救援有效开展的必要前提。震后应急物资运输与配送包括从区域储备仓库和外围物资集散地运输到区域应急配送中心、由区域应急配送中心配送到各个受灾点两个阶段。应急物资保障具有持续时间长、需求紧迫性强和物资相对短缺等特点。本文以应急物资保障时效性与分配公平性为目标,建立了考虑多种运输方式、多时段动态的应急物资配送中心选址与运输配送路径优化的多目标规划模型,并基于此模型设计了一种带精英策略的非支配排序的遗传算法,以九寨沟地区地震灾难情景对模型进行实证研究,验证了模型和算法的有效性,以期为震后应急物资保障提供理论指导与决策支持。     

知识分布、衰减程度与产业集群创新模式的内在机制研究

以知识流动为研究视角,将集群创新过程中的知识溢出、吸收能力内生化,构建包含三种创新模式下的产业集群演化模型,重点分析集群知识分布和知识衰减程度与集群创新模式的内在机制及其对创新产出的影响,并进行仿真实验。研究表明：不同知识分布下,混合创新模式的创新产出表现最好,外部吸收创新模式与内部自主创新模式呈现出不同的创新产出演化路径;在外部吸收创新和混合创新模式下,均匀分布型产业集群的创新产出表现最好,集中分布型表现次之,异质分布型表现最差,而在内部自主创新模式下,集中分布型则表现最好;不同创新模式对知识衰减的敏感程度呈现阶段性差异特征。这些结论对产业集群创新和可持续发展具有很好的理论和实践指导意义。     

第三次分配中慈善捐赠的经验性探讨

国外慈善捐赠看似微不足道,却对于西方社会的稳态定型起到巨大的作用.文章分析了国内慈善捐赠中所存在的问题,针对社会转型期的中国实际,提出了一些关于加强第三领域管理的建设性意见和引领性举措.