基于EM算法的改进MLM模型及参数估计

传统的分层模型假设组与组之间独立,没有考虑组之间的相关性。而以地理单元分组的数据往往具有空间依赖性,个体不仅受本地区的影响,也可能受相邻地区的影响。此时,传统分层模型层-2残差分布的假设不再成立。为了处理空间分层数据,将空间统计和空间计量经济模型的思想引入到分层模型中,既纳入分层的思想,又顾及空间相关性,提出了空间分层线性模型,并给出了其固定效应、方差协方差成分和空间回归参数的最大似然估计,在运用EM算法时,结合运用了Fisher得分算法。     

空间断点回归交互效应模型设定方法及其应用

空间断点回归实现因果关系统计推断的实验场景决定了交互效应是其固有特征。已有交互效应模型设定方法未充分考虑变量间的空间关系,使其应用局限于微观领域。文章在空间断点回归分析框架下,提出了模型化交互效应的更一般化方法,将其应用推广至区际差异分析等宏观领域,充分讨论了能够识别交互效应的主要统计假设、模型设定方法和统计推断方法,以及忽略交互效应产生的影响。基于此方法,对重庆市升级为直辖市的经济效应进行实验设计、统计推断和评价。实证分析表明,当恰当利用交互效应模型进行统计推断时,处理效应和交互效应估计值均显著为正,处理效应t检验显著性更强,且其估计值在一定空间范围内显著大于普通空间断点回归模型的估计值,该结论基本稳健。     

环境质量与居民生活满意度的实证分析

文章基于一个微观的生活满意度函数建立Ordered Logit模型,利用中国综合社会调查（CGSS）和中国统计年鉴的数据,对我国18个城市的居民生活满意度与空气环境质量之间的关系进行了实证分析。研究的结果表明,样本城市居民生活满意度与空气环境质量存在显著的相关关系,但是空气质量下降的影响主要体现在对低收入阶层居民的影响上,对高收入阶层的居民没有显著影响。文章的政策含义在于：改善环境质量,一方面可以直接提高居民的幸福感,另一方面可以通过促进社会公平而间接提高居民幸福感。     

城镇居民生活满意度模型与实证研究

在现有国际主流顾客满意度模型的基础上，构建了居民生活满意度模型，以内蒙古城镇居民为例进行了实证研究。并进一步扩展到对居民生活状况的总体把握，以致能够对影响居民生活及其满意度的微观和宏观生活领域的各方面的现状及问题作出一定独特视野与科学深度的分析与透视，从而为把握现实情况、解决现实问题提供更有效的参考作用。     

空间动态面板模型的时间滞后效应检验

空间面板数据模型由于考虑了经济变量间的空间相关性,其优势日益凸显,已成为计量经济学的热点研究领域。将空间相关性与动态模式同时扩展到面板模型中的空间动态面板模型,不仅考虑了经济变量之间的空间相关性,还考虑了时间上的滞后性,是空间面板模型的发展,增强了模型的解释力。考虑一种带固定个体效应、因变量的时间滞后项、因变量与随机误差项均存在空间自相关性的空间动态面板回归模型,提出了在个体数n和时间数T都很大,且T相对地大于n的条件下空间动态面板模型中时间滞后效应存在性的LM和LR检验方法,其检验方法包括联合检验、一维及二维的边际和条件检验;推导出这些检验在零假设下的极限分布;其极限分布均服从卡方分布。通过模拟试验研究检验统计量的小样本性质,结果显示其具有优良的统计性质。     

中国省域金融集聚的影响因素分析

文章基于中国省域面板数据,利用空间统计方法检验金融集聚的空间相关性,借鉴新经济地理学理论构建空间计量模型,结合动态空间面板模型的计量方法,对金融集聚相关影响因素进行实证分析.研究结果表明,我国的区域金融集聚效应存在较为显著的“空间自相关性”和“路径依赖”特性;地方保护阻碍了金融要素的跨区域流动;受“路径依赖”的影响,产业集聚对金融集聚的影响不明显;“城市拥挤效应”强于“城市经济效应”,导致城市化进程与金融集聚路径相悖;外贸依存和人才机制显著地促进金融集聚.     

我国城市环境库兹涅茨曲线的空间计量检验

文章基于1994～2005年我国46个不同类型城市空气质量和经济增长的面板数据,首次采用空间计量模型对我国城市EKC间的空间依赖关系进行了系统分析,实证结果发现:环境库兹涅茨曲线的估计结果很大程度上取决于计量模型和估计方法的选取。     

空间分层数据无条件模型的改进及其估计

抽样调查中得到的数据经常既包含个体信息又包含地理单元信息,形成以地区集聚的分层数据.空间分层数据中地理单元间往往具有空间依赖性,区别于传统的分层数据.分析空间分层数据时需要首先建立无条件模型用作初步分析.因此,在传统分层无条件模型中引入完全空间自回归模型来表达空间相关性,建立空间分层数据的无条件模型,并研究其估计方法,借助参数估计值可做模型选择.     

不同空间权重矩阵下文化产业集聚机制和溢出效应——基于2004—2011年省际面板数据的实证

在综合考虑多种要素对文化产业空间集聚协同作用的基础上,构建四种不同空间权重矩阵下的空间面板计量模型,实证研究文化产业空间集聚机制及溢出效应。研究表明:在纳入经济因素的空间权重矩阵下,中国文化产业集聚存在正的空间溢出效应,说明具有相似经济属性的地区文化产业存在示范效应,而地理位置上的"邻接"不能显著地促进地区间文化产业聚集与发展;各影响因素对文化产业空间集聚的直接效应和溢出效应也不尽相同,直接效应是文化产业集聚的核心机制。     

贝叶斯时空分位回归模型及其对北京市PM2.5浓度的研究

本文基于时空模型和非对称拉普拉斯分布提出一种新的时空分位回归模型.本文主要将空间域利用薄板回归样条展开,结合混合模型与样条之间的关系,得到分层贝叶斯分位回归模型.利用MCMC算法得到参数的后验分布,并对模型中系数的空间域进行预测.本文同时融合降秩近似的方法,简化了计算复杂度.区别于已有时空分位模型,本文考虑了协变量对因变量影响的空间分布特征,并非直接对时间或空间效应整体进行建模,有利于深入研究协变量与因变量之间的空间结构关系.数值模拟结果表明,预测的空间域与真实的空间域十分接近,并在不同分位水平下,有效地估计了协变量影响的空间效应差异.最后将该模型应用于北京市PM2.5浓度的研究,分析气象因素对PM2.5浓度影响的空间分布特征.     

COMPOSITIONAL TIME SERIES ANALYSIS OF MORTALITY PROPORTIONS

Compositional time series are multivariate time series which at each time point are proportions that sum to a constant. Accurate inference for such series which occur in several disciplines such as geology, economics and ecology is important in practice. Usual multivariate statistical procedures ignore the inherent constrained nature of these observations as parts of a whole and may lead to inaccurate estimation and prediction. In this article, a regression model with vector autoregressive moving average (VARMA) errors is fit to the compositional time series after an additive log ratio (ALR) transformation. Inference is carried out in a hierarchical Bayesian framework using Markov chain Monte Carlo techniques. The approach is illustrated on compositional time series of mortality events in Los Angeles in order to investigate dependence of different categories of mortality on air quality.     

城市化的经济发展效应——基于经济增长效应和空间溢出效应的分解分析视角

基于城市化经济运行理论,创新性地将城市化划分为"城"、"市"、"城市化"三个层次,并分别构建这三个层次的城市化的经济增长效应测度模型和空间溢出效应测度模型;借助于面板数据和空间面板数据的经济计量学分析方法,分别测度2001—2010年期间,中国35座副省级以上城市的城市化经济增长效应和空间溢出效应。结果表明:每个层次下各指标的这两种经济发展效应各不相同,城市化空间溢出效应多为"被动传导型",故应充分认识各城市化层次的经济增长效应和空间溢出效应的差异性,从中把握城市化经济发展效应的真实发挥状况,找到提升城市化效率的针对性途径,促进增长效应和空间溢出效应的发挥,带动城市协同发展。     

Order-free co-regionalized areal data models with application to multiple-disease mapping

With the ready availability of spatial databases and geographical information system software, statisticians are increasingly encountering multivariate modelling settings featuring associations of more than one type: spatial associations between data locations and associations between the variables within the locations. Although flexible modelling of multivariate point-referenced data has recently been addressed by using a linear model of co-regionalization, existing methods for multivariate areal data typically suffer from unnecessary restrictions on the covariance structure or undesirable dependence on the conditioning order of the variables. We propose a class of Bayesian hierarchical models for multivariate areal data that avoids these restrictions, permitting flexible and order-free modelling of correlations both between variables and across areal units. Our framework encompasses a rich class of multivariate conditionally autoregressive models that are computationally feasible via modern Markov chain Monte Carlo methods. We illustrate the strengths of our approach over existing models by using simulation studies and also offer a real data application involving annual lung, larynx and oesophageal cancer death-rates in Minnesota counties between 1990 and 2000.     

A novel and fast methodology for simultaneous multiple structural break estimation and variable selection for nonstationary time series models

A class of nonstationary time series such as locally stationary time series can be approximately modeled by piecewise stationary autoregressive (PSAR) processes. But the number and locations of the piecewise autoregressive segments, as well as the number of nonzero coefficients in each autoregressive process, are unknown. In this paper, by connecting the multiple structural break detection with a variable selection problem for a linear model with a large number of regression coefficients, a novel and fast methodology utilizing modern penalized model selection is introduced for detecting multiple structural breaks in a PSAR process. It also simultaneously performs variable selection for each autoregressive model and hence the order selection. To further its performance, an algorithm is given, which remains very fast in computation. Numerical results from simulation and a real data example show that the algorithm has excellent empirical performance.     

Hierarchical Bayesian bivariate disease mapping: analysis of children and adults asthma visits to hospital

In spatial epidemiology, detecting areas with high ratio of disease is important as it may lead to identifying risk factors associated with disease. This in turn may lead to further epidemiological investigations into the nature of disease. Disease mapping studies have been widely performed with considering only one disease in the estimated models. Simultaneous modelling of different diseases can also be a valuable tool both from the epidemiological and also from the statistical point of view. In particular, when we have several measurements recorded at each spatial location, one can consider multivariate models in order to handle the dependence among the multivariate components and the spatial dependence between locations. In this paper, spatial models that use multivariate conditionally autoregressive smoothing across the spatial dimension are considered. We study the patterns of incidence ratios and identify areas with consistently high ratio estimates as areas for further investigation. A hierarchical Bayesian approach using Markov chain Monte Carlo techniques is employed to simultaneously examine spatial trends of asthma visits by children and adults to hospital in the province of Manitoba, Canada, during 2000–2010.     

A simulation comparison on spatial Poisson mixed models

The statistical methods for analyzing spatial count data have often been based on random fields so that a latent variable can be used to specify the spatial dependence. In this article, we introduce two frequentist approaches for estimating the parameters of model-based spatial count variables. The comparison has been carried out by a simulation study. The performance is also evaluated using a real dataset and also by the simulation study. The simulation results show that the maximum likelihood estimator appears to be with the better sampling properties.     

A Bayesian hierarchical spatio-temporal rainfall model

A Bayesian hierarchical spatio-temporal rainfall model is presented and analysed. The model has the ability to deal with extensive missing or null values, uses a sophisticated variance stabilising rainfall pre-transformation, incorporates a new elevation model and can provide sub-catchment rainfall estimation and interpolation using a sequential kriging scheme. The model uses a vector autoregressive stochastic process to represent the time dependence of the rainfall field and an exponential covariogram to model the spatial correlation of the rainfall field. The model can be readily generalised to other types of stochastic processes. In this paper, some results of applying the model to a particular rainfall catchment are presented.     

A close look at the spatial structure implied by the CAR and SAR models

Modeling spatial interactions that arise in spatially referenced data is commonly done by incorporating the spatial dependence into the covariance structure either explicitly or implicitly via an autoregressive model. In the case of lattice (regional summary) data, two common autoregressive models used are the conditional autoregressive model (CAR) and the simultaneously autoregressive model (SAR). Both of these models produce spatial dependence in the covariance structure as a function of a neighbor matrix W and often a fixed unknown spatial correlation parameter. This paper examines in detail the correlation structures implied by these models as applied to an irregular lattice in an attempt to demonstrate their many counterintuitive or impractical results. A data example is used for illustration where US statewide average SAT verbal scores are modeled and examined for spatial structure using different spatial models.     

Spatial modelling for binary data using␣a␣hidden conditional autoregressive Gaussian process: a multivariate extension of the probit model

A Bayesian approach to modelling binary data on a regular lattice is introduced. The method uses a hierarchical model where the observed data is the sign of a hidden conditional autoregressive Gaussian process. This approach essentially extends the familiar probit model to dependent data. Markov chain Monte Carlo simulations are used on real and simulated data to estimate the posterior distribution of the spatial dependency parameters and the method is shown to work well. The method can be straightforwardly extended to regression models.