On smoothness of Tukey depth contours

The smoothness of Tukey depth contours is a regularity condition often encountered in asymptotic theory, among others. This condition ensures that the Tukey depth fully characterizes the underlying multivariate probability distribution. In this paper we demonstrate that this regularity condition is rarely satisfied. It is shown that even well-behaved probability distributions with symmetrical, smooth and (strictly) quasi-concave densities may have non-smooth Tukey depth contours, and that the smoothness behaviour of depth contours is fairly unpredictable.     

Predictive Inference for Big,Spatial, Non‐Gaussian Data: MODIS Cloud Data and its Change‐of‐Support

Remote sensing of the earth with satellites yields datasets that can be massive in size, nonstationary in space, and non‐Gaussian in distribution. To overcome computational challenges, we use the reduced‐rank spatial random effects (SRE) model in a statistical analysis of cloud‐mask data from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on board NASA's Terra satellite. Parameterisations of cloud processes are the biggest source of uncertainty and sensitivity in different climate models’ future projections of Earth's climate. An accurate quantification of the spatial distribution of clouds, as well as a rigorously estimated pixel‐scale clear‐sky‐probability process, is needed to establish reliable estimates of cloud‐distributional changes and trends caused by climate change. Here we give a hierarchical spatial‐statistical modelling approach for a very large spatial dataset of 2.75 million pixels, corresponding to a granule of MODIS cloud‐mask data, and we use spatial change‐of‐Support relationships to estimate cloud fraction at coarser resolutions. Our model is non‐Gaussian; it postulates a hidden process for the clear‐sky probability that makes use of the SRE model, EM‐estimation, and optimal (empirical Bayes) spatial prediction of the clear‐sky‐probability process. Measures of prediction uncertainty are also given.     

Inference on a progressive type I interval-censored truncated normal distribution

In this paper, we consider the problem of making statistical inference for a truncated normal distribution under progressive type I interval censoring. We obtain maximum likelihood estimators of unknown parameters using the expectation-maximization algorithm and in sequel, we also compute corresponding midpoint estimates of parameters. Estimation based on the probability plot method is also considered. Asymptotic confidence intervals of unknown parameters are constructed based on the observed Fisher information matrix. We obtain Bayes estimators of parameters with respect to informative and non-informative prior distributions under squared error and linex loss functions. We compute these estimates using the importance sampling procedure. The highest posterior density intervals of unknown parameters are constructed as well. We present a Monte Carlo simulation study to compare the performance of proposed point and interval estimators. Analysis of a real data set is also performed for illustration purposes. Finally, inspection times and optimal censoring plans based on the expected Fisher information matrix are discussed.     

城市停车产业化的投资激励机制探索

城市停车难已经成为制约城市治理的重要问题,停车产业化是解决城市停车难题的重要路径。停车产业化应明确界定政府、市场和社会之间的边界,加强城市停车供给侧结构性改革。增加停车供给可以通过政府机制或市场机制来实现,但市场机制具有天然的优势。当前,要积极发挥市场机制在资源配置中的决定性作用,积极推进停车资源的市场化供给;根据停车资源产业化、民营化和专业化的基本要求,积极营造良好的停车市场投资环境,鼓励民间资本的市场化经营;以民间资本的投资经营利润为激励机制,构建有利于停车资源时空配置的合理价格体系。     

An omnibus two-sample test for ranked-set sampling data

We develop an omnibus two-sample test for ranked-set sampling (RSS) data. The test statistic is the conditional probability of seeing the observed sequence of ranks in the combined sample, given the observed sequences within the separate samples. We compare the test to existing tests under perfect rankings, finding that it can outperform existing tests in terms of power, particularly when the set size is large. The test does not maintain its level under imperfect rankings. However, one can create a permutation version of the test that is comparable in power to the basic test under perfect rankings and also maintains its level under imperfect rankings. Both tests extend naturally to judgment post-stratification, unbalanced RSS, and even RSS with multiple set sizes. Interestingly, the tests have no simple random sampling analog.     

额外资源按需分配方法研究

文献Wei Quanling等^[1]的最后一段提出了这样一类额外资源分配问题：假设有一些额外的投入资源将要分配给全部或者部分决策单元,如果我们希望分配结果对于整个系统（该系统由全部决策单元组成）来说是最有益的,那么应当如何分配这些额外的投入资源?他们同时还指出系统中哪些决策单元能够获得额外的投入资源,这不仅取决于决策单元的效率,还需要考虑规模报酬。针对此类问题,本文提出一种额外资源按需分配方法：首先,综合决策单元的技术效率与其投影点的规模弹性构建发展曲线,并由决策单元的发展曲线获知其投入变动与产出变动的对应关系;然后,把额外资源分成若干等份,依次分配每一个等份的额外资源,每次分配使系统的总产出增加实现最大,直至分配完全部的额外资源,或者分配某一份额外资源不再带来任何的产出增加,分配就会停止;最后,把决策单元在每一次分配过程中获得的额外资源进行累加,得到这个决策单元最终的分配结果。全文的最后分别在"一个投入一个产出"和"两个投入一个产出"情况下给出两个具体的分配算例。分配结果表明,这种按需分配方法不会缩减任意一个决策单元当前占有的投入资源数量,并且还可以有效地在辨识出某种额外资源在规模上和结构上存在的冗余,从而有效地避免不必要的分配。     

一类稀疏投资组合双层参数估计模型及其应用

带基约束的投资组合问题是近年来投资组合领域的热点问题,但是参数不确定性直接影响了模型的效果。带基约束的投资组合问题所涉及的参数不仅包括以往研究认为非常重要的预期收益率,还包括控制投资组合规模的稀疏度,尤其是最优稀疏度估计方面的专门研究还十分匮乏。为了使带基约束的投资组合模型更好地为投资决策服务,本文从投资者效用出发,用双层规划的思想构建了带基约束的投资组合双层参数估计模型。然后根据模型的特点,设计了无导数优化算法框架,并基于ADMM对算法子问题进行求解。本文实验针对真实的市场数据给出了预期收益率和最优稀疏度的估计,接着通过与等权重策略和含上下界约束的均值-方差模型进行比较,说明了模型及算法的有效性和实用性。最后,将本文提出的双层参数估计模型推广到了更一般的形式。     

盐业企业人力资源优化配置的探讨——以四川久大品种盐有限责任公司为例

四川久大盐业（集团）公司是目前中国规模最大、配套最全的井矿盐生产企业集团,品种盐是四川久大盐业（集团）公司旗下的一个全资子公司,品种盐有限公司在人力资源配置方面进行了一些改革,并收到了一定的效果。但要对人力资源进行优化配置不是一件容易的事情,就其现有的人力资源配置情况也存在诸多问题,表现为年龄结构老龄化、知识结构不合理、性别结构不合理等。针对这些问题,可从调整员工年龄结构、加强员工培训力度、调整性别配置比例等方面入手,解决盐业企业人力资源优化配置的问题。     

校院两级管理模式下的教育资源优化配置

校院两级管理模式是一种不同于高校传统管理方式的新模式,如何在校院两级管理模式下对学校内部教育资源进行优化配置,一直是我国高校管理工作者希望解决的问题。首先通过明晰校院两级的职能界定,提出校院两级管理模式下“两级管理,提高绩效,培育特色,形成机制”的教育资源优化配置原则;并从权责利明晰、制衡有度,资源配置市场化、效益化,目标管理规范到位,评价机制科学合理,财务分配制度灵活高效等五个方面研究校院两级管理模式下教育资源优化配置的运行机制。     

论北魏自耕农的贫困

自耕农的贫困始终是困扰北魏政权的重要问题。北魏自耕农来源的多元化与致贫因素的复杂化使得北魏自耕农负担不断加重。北魏自耕农的贫困与贫富差距严重的主要原因在于北魏制度设计的缺陷。北魏政权为防止自耕农贫困而采取的赋税蠲免、灾伤赈济以及税率调控等措施仅是头痛医头之举,并未从制度上解决根本问题,因此也无法从根本上缓解自耕农的贫困。这最终导致了北魏的衰亡。