Data skeletons: simultaneous estimation of multiple quantiles for massive streaming datasets with applications to density estimation

We consider the problem of density estimation when the data is in the form of a continuous stream with no fixed length. In this setting, implementations of the usual methods of density estimation such as kernel density estimation are problematic. We propose a method of density estimation for massive datasets that is based upon taking the derivative of a smooth curve that has been fit through a set of quantile estimates. To achieve this, a low-storage, single-pass, sequential method is proposed for simultaneous estimation of multiple quantiles for massive datasets that form the basis of this method of density estimation. For comparison, we also consider a sequential kernel density estimator. The proposed methods are shown through simulation study to perform well and to have several distinct advantages over existing methods.     

On Rates of Convergence for Bayesian Density Estimation

Abstract.  We consider the problem of estimating a compactly supported density taking a Bayesian nonparametric approach. We define a Dirichlet mixture prior that, while selecting piecewise constant densities, has full support on the Hellinger metric space of all commonly dominated probability measures on a known bounded interval. We derive pointwise rates of convergence for the posterior expected density by studying the speed at which the posterior mass accumulates on shrinking Hellinger neighbourhoods of the sampling density. If the data are sampled from a strictly positive, α -Hölderian density, with α  ∈ ( 0,1] , then the optimal convergence rate n^{− α / (2 α +1)} is obtained up to a logarithmic factor. Smoothing histograms by polygons, a continuous piecewise linear estimator is obtained that for twice continuously differentiable, strictly positive densities satisfying boundary conditions attains a rate comparable up to a logarithmic factor to the convergence rate n ^−4/5 for integrated mean squared error of kernel type density estimators.     

Improved Monte Carlo inference for models with additive error

Some statistical models defined in terms of a generating stochastic mechanism have intractable distribution theory, which renders parameter estimation difficult. However, a Monte Carlo estimate of the log-likelihood surface for such a model can be obtained via computation of nonparametric density estimates from simulated realizations of the model. Unfortunately, the bias inherent in density estimation can cause bias in the resulting log-likelihood estimate that alters the location of its maximizer. In this paper a methodology for radically reducing this bias is developed for models with an additive error component. An illustrative example involving a stochastic model of molecular fragmentation and measurement is given.     

西安环境污染经济损失估算与分析

为有效监测、控制环境污染,保护城市环境,采用市场价值法、修正人力资本法和工程费用法等估算方法,对2002年西安市大气污染、水污染和固体废弃物污染造成的经济损失进行测算评估,通过各项指标和数据分析环境污染状况,给出西安市因大气、水和固体废弃物污染造成的经济损失估算结果。结论认为,西安市环境状况依然十分严峻,2002年因环境污染造成的直接经济损失高达7.30亿元。城市污染治理和城市环境保护由此成为西安市经济可持续发展的首要前提。     

论可持续发展战略下环境成本的计量方法

随着经济的不断发展,环境污染的日益加重,人们也越来越重视环境会计的研究。环境会计在处理数据、生成和传递环境信息的过程中,所涉及的关键问题便是环境会计的计量。而环境成本的计量又是环境会计研究中最重要的一环,在实践中可运用环境成本计量的基本方法及基本的计量模型。正确计量环境成本是环境成本核算的前提,对企业拥有的自然资源中可以计量的部分进行合理的估计,有利于满足社会各方面对环境问题提出的客观要求。     

网络密度、高管注意力配置与企业绿色创新:制度压力的调节作用

本文整合社会网络理论、注意力基础观和新制度主义理论等,通过对282家制造企业的问卷调查,采用多元回归分析方法,探讨了网络密度和高管注意力配置的交互匹配对企业绿色创新的影响机制,以及制度压力在其中的调节作用。研究发现,在高密度网络中,高管采用注意力聚集策略对绿色创新有显著正向影响;而在低密度网络中,高管采用注意力分散策略对绿色创新有显著正向影响。规制压力和模仿压力均正向调节网络密度和高管注意力配置的交互作用对绿色创新的影响,但模仿压力的调节作用更强。通过构建不同制度压力情景下网络密度与高管注意力的交互作用对绿色创新影响的理论框架,本文推动社会网络理论、注意力基础观等成熟的理论成果从一般创新向绿色创新研究领域繁衍,为企业绿色创新加入与主流理论的对话做了努力,为企业在绿色创新过程中根据不同的制度压力,以及所嵌入的组织间网络密度情况,选择合适的注意力配置策略提供理论参考。     

财政分权、产业结构调整与绿色全要素生产率——基于环境污染门槛效应的视角

基于财政分权下政府与产业结构调整的策略博弈,从理论模型分析了政府产业引导与产业自身调整间的非合作博弈的发生、造成产业结构调整在绿色全要素生产率上的异质化效应,并应用2004—2017年间中国省级面板数据,采用动态面板门槛模型进行了实证检验。研究结果表明:当环境污染程度较低时,财政分权的施行将有助于产业结构调整带动绿色全要素生产率提升;当环境污染程度较高时,财政分权的施行将不利于促进产业结构调整带动绿色全要素生产率提升;财政分权主要通过税收竞争与财政环境补贴和支出双重路径影响产业绿色全要素生产率的提升。借此,对在区域财政分权背景下动态调整区域财政体制与产业绿色发展转型提出了相应的建议。     

过度旅游的综合评价方法及其实证研究

针对过度旅游对旅游目的地造成紊乱的问题,对过度旅游的成因及危害进行总结分析,利用统计数据确定综合旅游密度、旅游强度、民宿密度3项判断过度旅游状态的指标,并将3项指标分数之和作为评价过度旅游的指标值; 使用Zipf分布双对数图与H/T断裂点法确定数据等级划分方式,采用定距分数累加方式建立过度旅游综合评价方法; 利用国内外代表性旅游城市的数据进行了实证分析,验证了所建立综合评价方法的可行性。研究认为,过度旅游综合评价分数给出了旅游目的地过度旅游程度的相对顺序,能够推测各样本过度旅游风险所对应的要素,为制定过度旅游对应政策的优先级提供参考; 在样本城市中,国外过度旅游目的地注重流量管控、征收旅游税等三四级对策,而中国过度旅游目的地注重景区保护和流量管控等二三级对策,尽管国内样本城市旅游服务负荷已高于部分发达国家,但未出现排斥游客的过度旅游现象; 国内仍需要利用经济手段避免部分地区的旅游业依赖,升级城市景区保护为城市全域保护,适时推进旅游税政策。     

Approximation of density functions by orthogonal series with grouped data

Summary Letg(x) andf(x) be continuous density function on (a, b) and let {ϕ_j} be a complete orthonormal sequence of functions onL ₂(g), which is the set of squared integrable functions weighted byg on (a, b). Suppose that over (a, b). Given a grouped sample of sizen fromf(x), the paper investigates the asymptotic properties of the restricted maximum likelihood estimator of density, obtained by setting all but the firstm of the ϑ_j’s equal to0. Practical suggestions are given for performing estimation via the use of Fourier and Legendre polynomial series. Research partially supported by: CNR grant, n. 93. 00837. CT10.     

A simple,automatic and adaptive bivariate density estimator based on conditional densities

The standard approach to non-parametric bivariate density estimation is to use a kernel density estimator. Practical performance of this estimator is hindered by the fact that the estimator is not adaptive (in the sense that the level of smoothing is not sensitive to local properties of the density). In this paper a simple, automatic and adaptive bivariate density estimator is proposed based on the estimation of marginal and conditional densities. Asymptotic properties of the estimator are examined, and guidance to practical application of the method is given. Application to two examples illustrates the usefulness of the estimator as an exploratory tool, particularly in situations where the local behaviour of the density varies widely. The proposed estimator is also appropriate for use as a pilot estimate for an adaptive kernel estimate, since it is relatively inexpensive to calculate.