Designing and integrating composite networks for monitoring multivariate gaussian pollution fields

Networks of ambient monitoring stations are used to monitor environmental pollution fields such as those for acid rain and air pollution. Such stations provide regular measurements of pollutant concentrations. The networks are established for a variety of purposes at various times so often several stations measuring different subsets of pollutant concentrations can be found in compact geographical regions. The problem of statistically combining these disparate information sources into a single 'network' then arises. Capitalizing on the efficiencies so achieved can then lead to the secondary problem of extending this network. The subject of this paper is a set of 31 air pollution monitoring stations in southern Ontario. Each of these regularly measures a particular subset of ionic sulphate, sulphite, nitrite and ozone. However, this subset varies from station to station. For example only two stations measure all four. Some measure just one. We describe a Bayesian framework for integrating the measurements of these stations to yield a spatial predictive distribution for unmonitored sites and unmeasured concentrations at existing stations. Furthermore we show how this network can be extended by using an entropy maximization criterion. The methods assume that the multivariate response field being measured has a joint Gaussian distribution conditional on its mean and covariance function. A conjugate prior is used for these parameters, some of its hyperparameters being fitted empirically.     

Toward an integrated Bayesian network approach to measurement error detection and correction

In this article, the quality of data produced by national statistical institutes and by governmental institutions is considered. In particular, the problem of measurement error is analyzed and an integrated Bayesian network decision support system based on non-parametric Bayesian networks is proposed for its detection and correction. Non-parametric Bayesian networks are graphical models expressing dependence structure via bivariate copulas associated to the edges of the graph. The network structure and the misreport probability are estimated using a validation sample. The Bayesian network model is proposed to decide: (i) which records have to be corrected; (ii) the kind and amount of correction to be adopted. The proposed correction procedure is applied to the Banca d’Italia Survey on Household Income and Wealth and, specifically, the bond amounts are analyzed. Finally, the sensitivity of the conditional distribution of the true value random variable given the observed one to different evidence configurations is studied.     

Estimating E-bayesian and hierarchical bayesian of scalar parameter of gompertz distribution under type II censoring schemes based on fuzzy data

In this study, the E-Bayesian and hierarchical Bayesian of the scalar parameter of a Gompertz distribution under Type II censoring schemes were estimated based on fuzzy data under the squared error (SE) loss function and the efficiency of the proposed methods was compared with each other and with the Bayesian estimator using Monte Carlo simulation.     

Using logic model and visualization to conduct portfolio evaluation

Portfolio evaluation is the evaluation of multiple projects with a common purpose. While logic models have been used in many ways to support evaluation, and data visualization has been used widely to present and communicate evaluation findings, adopting logic models for portfolio evaluation and using data visualization to share findings simultaneously is surprisingly limited in the literature. With the data from a sample portfolio of 209 projects which aims to improve the system of early care and education (ECE), this study illustrated how to use logic model and data visualization techniques to conduct a portfolio evaluation by answering two evaluation questions: “To what extent are the elements of a logic model (strategies, sub-strategies, activities, outcomes, and impacts) reflected in the sample portfolio?” and “Which dominant paths through the logic model were illuminated by the data visualization technique?” For the first question, the visualization technique illuminated several dominant strategies, sub-strategies, activities, and outcomes. For the second question, our visualization techniques made it convenient to identify critical paths through the logic model. Implications for both program evaluation and program planning were discussed.     

Multiple hypothesis tests based On conditional differences in means

Many hypothesis tests are univariate tests and cannot cope with multiple hypothesis without an auxiliary procedure as e. g. the Bonferroni-Holm-procedure. At the same time, there is an urgent need for testing multiple hypothesis due to the very simple existing methods as the Bonferroni-correction or the Bonferroni-Holm-procedure, which suffers from a very small local significance level to detect statistical inferences or the drawback that logical and statistical dependencies among the test statistics are not used, whereby its detection is NP-hard. In honour of this occasion, we present a multiple hypothesis test for i.i.d. random variables based on conditional differences in means, which is capable to cope with multiple hypothesis and does not suffer on such drawbacks as the Bonferroni-correction or the Bonferroni-Holm-procedure. Thereby, the computation time can be neglected.     

工业取用水监测奇异数据挖掘与重构方法

提高工业取用水监测数据质量是目前国家水资源监控能力建设的重要内容,而奇异值问题已成为影响监测数据质量的关键短板。本文在解析现阶段工业取用水监测数据奇异值主要类型基础上,以国家水资源管理系统数据库中工业取用水监测数据为样本,利用小波变换模极大值模型提取工业取用水监测数据时频变化特征,并利用傅里叶函数对其残差序列进行修正,进而运用相对误差控制方法挖掘监测数据奇异值。在此基础上,采用混沌粒子群优化的最小二乘支持向量机模型重构填补奇异值数据。研究结果表明：小波变换模极大值模型能够较好地提取工业取用水监测数据序列的时频变化特征,但是同时容易导致监测数据的信息损失,利用傅里叶函数对小波变换进行残差修正则可进一步提升取用水监测数据序列的特征提取效果;以小波变换模极大值特征序列为基础,通过相对误差控制可实现对监测数据奇异值的高效挖掘;对于挖掘出的奇异值重构填补问题,可选取混沌粒子群优化的最小二乘支持向量机模型,其重构精度要优于多项式曲线拟合等传统统计学方法和普通最小二乘支持向量机模型。上述工业取用水监测数据奇异值挖掘重构策略为现阶段国家水资源监控能力建设的推进提供了重要技术方法支持。     

大数据背景下网络调查样本的建模推断问题研究——以广义Boosted模型的倾向得分推断为例

随着大数据和网络的不断发展,网络调查越来越广泛,大部分网络调查样本属于非概率样本,难以采用传统的抽样推断理论进行推断,如何解决网络调查样本的推断问题是大数据背景下网络调查发展的迫切需求。本文首次从建模的角度提出了解决该问题的基本思路：一是入样概率的建模推断,可以考虑构建基于机器学习与变量选择的倾向得分模型来估计入样概率推断总体;二是目标变量的建模推断,可以考虑直接对目标变量建立参数、非参数或半参数超总体模型进行估计;三是入样概率与目标变量的双重建模推断,可以考虑进行倾向得分模型与超总体模型的加权估计与混合推断。最后,以基于广义Boosted模型的入样概率建模推断为例演示了具体解决方法。     

交通拥堵与雾霾污染的因果关系——基于收敛交叉映射技术的经验研究

交通拥堵与雾霾污染是制约现代城市发展的两大顽疾,准确识别交通拥堵与雾霾污染之间的交互影响,有助于城市管理者重新审视现行治堵与治霾政策的合理性。本文借助大数据平台采集了我国99个城市的高德拥堵延迟指数（CDI）、空气质量指数（AQI）及六种分项空气污染物浓度日报数据,首次采用收敛交叉映射（CCM）方法实证考察了交通拥堵与雾霾污染之间的因果关系。研究发现,CDI与AQI以及CDI与分项污染物组成的动态系统均呈现明显的非线性与弱耦合特征。基于CCM检验结果,大多数城市的CDI与AQI之间不存在显著的因果关系;从分项空气污染物的角度,大多数城市的CDI与主要空气污染物之间不存在显著因果关系,但与次要空气污染物之间却存在显著的单向或双向因果关系。上述结果表明,尽管交通拥堵与雾霾污染之间有一定关联,但在因果关系上现有的经验证据并不支持两者相互影响,治堵和治霾不能“一箭双雕”而必须“双管齐下”。本文的研究在经验上丰富了关于交通拥堵与雾霾污染交互影响的讨论,对城市管理者更加谨慎与合理地制定治堵政策与治霾政策有重要现实意义。     

新旧“大历史”：前提假设、范围与可能性

近年来,大卫·克里斯蒂安等人已经将“大历史”提升为一种研究过去的新方法。本文将大历史与旧大历史、也就是“普遍史”传统并列。“新”大历史或者以一种连续的神学为基础,或者建立在一种唯物主义形式之上,这种唯物主义被看作人类历史决定因素,或者起源于两者多少有些矛盾的混合。新旧大历史例示的“大跨度历史”与其说是对历史知识的贡献,不如说是这种或那种世界观的叙述化过程。在区分最大跨度历史与“仅仅是”大跨度历史过程中,在历史书写过程中满足大跨度历史要求的更好方法是更适度的尝试,例如,大跨度比较史、网络与交流史、专题史和现代化史。     

Blood,Sweat, and Tears: Navigating Creepy versus Cool in Wearable Biotech

ABSTRACT

This paper considers how personal data protections figure into the design process of wearable technology. The data question is becoming especially important in the face of recent innovations in biotechnology that capitalize on the new fungibility of biology and electronics, in which new biotech wearables capitalize on the ability to analyze and track changes in blood, sweat, and tears. Interviews and participant observation with wearable tech designers, data scientists, fashion tech entrepreneurs, and select experts in cybersecurity and intellectual property law, reveal a range of approaches to data protection in design within the culture where wearables are beginning to merge with biotech. While a few respondents were extremely vigilant about protecting consumer’s privacy, the majority felt that when consumers ‘opt in’ to data sharing, they should be cognizant of the risks. From their perspective, it is not necessarily the producer’s responsibility to protect user's personal data. These attitudes present a problematic logic, which leaves users vulnerable to data exploitation. The paper concludes by arguing that this laissez-faire culture is the environment in which wearable biotech is being developed and will be deployed. This emerging technology raises issues about bodies, data, and ownership in crucial need of analysis and critique to push its move into the mainstream toward more equitable and inclusive ends.