Building a statistical model to predict reactor temperatures

This paper describes the various stages in building a statistical model to predict temperatures in the core of a reactor, and compares the benefits of this model with those of a physical model. We give a brief background to this study and the applications of the model to rapid online monitoring and safe operation of the reactor. We describe the methods, of correlation and two dimensional spectral analysis, which we use to identify the effects that are incorporated in a spatial regression model for the measured temperatures. These effects are related to the age of the reactor fuel and the spatial geometry of the reactor. A remaining component of the temperature variation is a slowly varying temperature surface modelled by smooth functions with constrained coefficients. We assess the accuracy of the model for interpolating temperatures throughout the reactor, when measurements are available only at a reduced set of spatial locations, as is the case in most reactors. Further possible improvements to the model are discussed.     

A multivariate time series model for the analysis and prediction of carbon monoxide atmospheric concentrations

We use a Bayesian multivariate time series model for the analysis of the dynamics of carbon monoxide atmospheric concentrations. The data are observed at four sites. It is assumed that the logarithm of the observed process can be represented as the sum of unobservable components: a trend, a daily periodicity, a stationary autoregressive signal and an erratic term. Bayesian analysis is performed via Gibbs sampling. In particular, we consider the problem of joint temporal prediction when data are observed at a few sites and it is not possible to fit a complex space–time model. A retrospective analysis of the trend component is also given, which is important in that it explains the evolution of the variability in the observed process.     

Bayesian inference for generalized additive mixed models based on Markov random field priors

Most regression problems in practice require flexible semiparametric forms of the predictor for modelling the dependence of responses on covariates. Moreover, it is often necessary to add random effects accounting for overdispersion caused by unobserved heterogeneity or for correlation in longitudinal or spatial data. We present a unified approach for Bayesian inference via Markov chain Monte Carlo simulation in generalized additive and semiparametric mixed models. Different types of covariates, such as the usual covariates with fixed effects, metrical covariates with non-linear effects, unstructured random effects, trend and seasonal components in longitudinal data and spatial covariates, are all treated within the same general framework by assigning appropriate Markov random field priors with different forms and degrees of smoothness. We applied the approach in several case-studies and consulting cases, showing that the methods are also computationally feasible in problems with many covariates and large data sets. In this paper, we choose two typical applications.     

A marked point process perspective in fitting spatial point process models

This paper discusses a new perspective in fitting spatial point process models. Specifically the spatial point process of interest is treated as a marked point process where at each observed event x$\mathbf{x}$ a stochastic process M(x;t)$M(\mathbf{x};t)$, 0<t<r$0<t<r$, is defined. Each mark process M(x;t)$M(\mathbf{x};t)$ is compared with its expected value, say F(t;θ)$F(t;\theta )$, to produce a discrepancy measure at x$\mathbf{x}$, where θ$\theta$ is a set of unknown parameters. All individual discrepancy measures are combined to define an overall measure which will then be minimized to estimate the unknown parameters. The proposed approach can be easily applied to data with sample size commonly encountered in practice. Simulations and an application to a real data example demonstrate the efficacy of the proposed approach.     

Multistage sampling for latent variable models

I consider the design of multistage sampling schemes for epidemiologic studies involving latent variable models, with surrogate measurements of the latent variables on a subset of subjects. Such models arise in various situations: when detailed exposure measurements are combined with variables that can be used to assign exposures to unmeasured subjects; when biomarkers are obtained to assess an unobserved pathophysiologic process; or when additional information is to be obtained on confounding or modifying variables. In such situations, it may be possible to stratify the subsample on data available for all subjects in the main study, such as outcomes, exposure predictors, or geographic locations. Three circumstances where analytic calculations of the optimal design are possible are considered: (i) when all variables are binary; (ii) when all are normally distributed; and (iii) when the latent variable and its measurement are normally distributed, but the outcome is binary. In each of these cases, it is often possible to considerably improve the cost efficiency of the design by appropriate selection of the sampling fractions. More complex situations arise when the data are spatially distributed: the spatial correlation can be exploited to improve exposure assignment for unmeasured locations using available measurements on neighboring locations; some approaches for informative selection of the measurement sample using location and/or exposure predictor data are considered.     

政府公共支出与中国经济增长——基于VAR方法的研究

基于协整理论和向量误差修正模型,考察了政府公共支出对中国经济增长的长期影响及二者之间的互动关系.研究发现:(1)多项公共支出变量为非平稳的二阶单整并与总产出及全社会固定资本形成额等经济变量存在长期均衡关系;(2)非生产性支出与经济增长正相关,生产性支出、政府机构支出与经济增长负相关;(3)非生产性支出是经济增长的长期原因,而经济增长只是政府机构支出增加的长期原因.经济增长反方向促进非生产性支出和生产性支出增加在统计学上并不显著.     

论历史街区保护与再生的原则和手段

文章主要论述了历史街区在现代化城市建设中的重要价值;对其进行保护与再生的基本原则,即文化认同原则、保用结合原则和可持续性原则;历史街区保护与再生手段,包括对原有建筑物的保护与更新、对原有街区空间肌理的维护与扩展、对原有街区人文精神的继承与延续。     

金融集聚与江淮城市群城镇化空间计量分析

基于江淮城市群2002—2012年10个城市面板数据,构建金融集聚和人口城镇化相关的指标体系,通过计算金融集聚Moran's I得出江淮城市群具有空间正相关关系,并以此建立了金融集聚和人口城镇化空间滞后模型和空间误差模型。结果显示,江淮城市群金融集聚对人口城镇化发展有促进作用,且处于上升发展趋势。控制变量人口密度、二三产业从业人员比率对人口城镇化的发展有一定影响。     

环境规制能否有效驱动高质量发展？——基于人力资本视角的理论与经验分析

在加强生态文明建设和扩大就业的背景下,环境规制能否同时实现环境保护、绿色就业与高质量发展等多重目标已成为亟待解决的重大问题。构建一种操作便捷的高质量发展评价指标,将其分解为结构效应、技术效应及规制效应,在统一框架内剖析环境规制影响高质量发展的人力资本机制,并使用联立方程空间自回归模型和省际面板数据进行检验,探究在各类人力资本因素调节下环境规制对高质量发展的非线性效应。研究发现：（1）环境规制能通过人力资本积累、产业结构升级、节能减排技术创新等途径影响高质量发展。（2）环境规制通过与人力资本溢价的动态匹配,能进一步推动产业结构升级和节能减排技术创新,助益高质量发展。（3）环境规制在人力资本因素调节下对高质量发展存在非线性影响。总体而言,人力资本因素的改善有利于充分发挥环境规制对高质量发展的倒逼效应;提升人力资本质量是当前调控人力资本因素、释放环境规制高质量发展效应的政策重点。（4）中国高质量发展存在显著为正的空间溢出效应,地方政府在环境问题中缺乏协同治理,削弱了这种空间溢出效应,因此全局层面的高质量发展有较大提升空间。充分激发环境规制的人力资本积累效应,从单纯注重人力资本“量”的扩张转向“质”的提升,实现区域协同治理,这对有效改善环境规制的高质量发展效应至关重要。     

中国非寿险市场承保周期的存在性研究

承保周期源于保险供给与需求的周期性波动,它以承保利润、保险费率的周期性变化为主要特征。选取中国1980年～2006年的年度数据,采用二阶自回归模型和谱分析法两种方法来 检验中国整个非寿险市场是否存在承保周期现象。经检验发现,中国非寿险市场确实存在承保周期,且存在12.5年～ 16.7年的长承保周期和5.6年左右的中周期。该结果与亚洲非寿险市场的承保周期基本类似。