中年女性血红蛋白参考值与地理因素的非线性模型

文章收集了中国268个单位用氰化高铁血红蛋白(HiCN)法测定的25917例中年女性血红蛋白参考值,运用非线性回归分析的方法,研究了其与地理因素的关系,发现中年女性血红蛋白参考值与地理因素之间有很显著的相关关系,得到一个多元非线性回归方程。因此知道了某地的地理因素,就可以用非线性回归方程估算这个地区的血红蛋白参考值。依据血红蛋白参考值与地理因素的依赖关系可以把中国分为青藏区、西南区、西北区、东南区、华北区、东北区六个区,为制定中年女性血红蛋白参考值的统一标准提供科学了依据。     

基于空间计量经济的中国FDI区位分布决定因素研究

现有FDI区位问题的研究多强调各类传统区位因素的作用,却忽略了地理空间效应的影响.文章采用空间计量经济模型与普通计量经济模型对比分析的方法,对中国大陆省域FDI区域分布决定因素进行了研究.结果表明,中国FDI区域分布决定除受传统因素影响外,空间效应因素的作用也不可忽视,并对我国(尤其中西部地区)引进FDI具有重要政策启示.     

中国旅游产业生命周期判断及其时空格局演变

采用生长曲线模型和产出增长率法综合判断了中国旅游产业所处发展阶段,并运用探索性空间数据分析方法、重心模型和地理加权回归分析了中国旅游产业时空演变特征及驱动力。结果表明:中国旅游业目前正处在产业生命周期中的成长阶段,旅游重心空间位置相对比较稳定;中国旅游产业在空间上呈现正自相关关系,表现出一定的空间集聚分布特征,且空间集聚程度逐渐提高。局部空间格局演化特征明显,HH集聚区呈现出随中国旅游重心轨迹向中西部扩散的发展态势,LH集聚区空间分布分散,且趋于中西部集中,LL集聚区分布在西部地区,且空间范围呈不断缩小趋势,HL集聚区主要集中在东部和中部地区且分布范围最广;中国省域单元旅游空间结构较为稳定,空间集聚存在较高路径依赖,不同类型空间之间存在一定的转移惰性,较少发生跃迁;资源驱动、市场驱动、交通驱动、创新驱动是影响中国旅游业发展的主要驱动力,其影响程度具有明显的空间异质性。     

中国文化产业区域集聚的空间计量分析

文章运用空间计量经济模型对经济地理与产业政策因素对中国文化产业区域集聚影响进行了实证分析。研究结果发现:(1)中国文化产业存在较明显的空间集聚性和空间相关性,邻接地区间的文化产业集聚具有正的溢出效应;(2)沿海区位与文化资源禀赋能部分解释中国文化产业区域集聚,在控制新经济地理与产业政策因素影响下,经济地理因素对文化产业集聚的影响不再显著;(3)文化消费需求、文化企业数量、人力资本水平与城市化对文化产业集聚有正向影响;(4)政府的财政支持促进了文化产业集聚,金融服务对文化产业集聚的影响不显著。     

空间溢出视角下对外贸易和中国碳排放——基于半参数面板空间滞后模型

针对现有文献研究碳排放时忽略经济空间溢出效用以及对外贸易—碳排放非线性关系的缺憾,估算了2001-2011年中国29个省区碳排放量,然后构建半参数空间滞后模型,并结合工具变量法以解决对外贸易的内生性问题,实证研究了对外贸易对中国区域碳排放的影响。研究发现:(1)十年来中国贸易开放对碳排放的影响从促进逐渐开始转变为抑制。东部地区对外贸易发展较快,目前正处于逐渐抑制碳排放阶段;中部地区对外贸易水平较低,对外贸易的发展仍促进了碳排放的增加;西部地区对外贸易与碳排放的关系不明确。(2)半参数面板空间滞后模型比参数模型具有更高的拟合优度和更丰富的结论,它能够有效刻画碳经济现象中空间相关性与非线性特征。     

国家生态文明建设示范区时空特征及其影响因素分析

文章以2017—2021年生态环境部公布的五批共362个国家生态文明建设示范区为研究对象，采用平均最邻近指数、核密度分析、地理探测器和地理加权回归等方法，研究国家生态文明建设示范区的空间分布格局及其影响因素。结果显示：（1）示范区空间分布具有非均衡性特征；在省域层面，浙江的示范区最多，福建次之；在经济区层面，主要集中在东部沿海、长江中游、大西南和南部沿海四大经济区；从三大地区来看，东部地区占比最高。（2）就空间密度而言，示范区呈现由以上海、江苏、浙江为核心的东部地区向中西部地区扩散的趋势，且具有显著的经向地带性差异。（3）示范区具有空间正相关性，冷热点区存在显著的面状分布特征。（4）对外开放程度和人均人力资本负向影响示范区分布，人均居民可支配收入、人口密度和第二产业产值占比则对其具有正向影响。     

未被观测经济对中国环境污染的影响分析

现有关于环境污染的研究大都从官方经济视角展开,忽视了未被观测经济(NOE)的作用。文章利用2000~2012年30个地区的面板数据实证考察了NOE对中国环境污染的影响。研究发现,环境污染与NOE比率之间存在显著的U型关系,控制经济发展水平、产业结构、技术进步及贸易开放等因素后,U型曲线的转折点为16.98%。从全国层面来看,基于NOE比率视角的环境污染于2008年步入U型曲线的上升通道;从地区层面来看,中国有15个地区已经越过U型曲线的转折点。     

长三角地区共同富裕水平、区域差异及动态演进

开展共同富裕水平测度并研究其时空特征,可以为扎实推进共同富裕提供参考。文章基于2010—2020年长三角地区41个地级及以上城市的数据,运用熵权法从富裕程度、共享程度和可持续性三个维度测算共同富裕水平,并借助Dagum基尼系数及其分解、核密度估计、Markov转移概率矩阵和地理探测器分析区域差异和分布动态演进。研究发现:长三角地区共同富裕水平和三个子维度水平在考察期内总体上均呈现上升趋势且存在显著的地区差异。其中,地区相对差异逐渐缩小,区域间差异是其主要来源;各城市间绝对差异逐渐扩大,共同富裕各等级城市均对其前期共同富裕水平存在较强的路径依赖;收入与消费水平差异是区域差异在指标层面上的重要来源。     

地理空间效应下中国区域金融发展的俱乐部趋同研究——对321个地级市的实证分析

在传统趋同研究的基础上,通过外生和内生两种区域类型划分方法,研究了2003—2010年中国321个地级市金融发展在地理空间效应视角下的俱乐部趋同特征。研究表明,纳入地理空间效应的趋同模型比传统模型更优,金融发展的空间溢出效应明显;外生法下中国金融发展的趋同存在俱乐部效应,东部趋同速度远大于中西部;内生法下的高值聚集区域存在更显著的俱乐部趋同,但位于西北的低值聚集区域却不存在趋同。所以初始条件和结构特征落后的地区应积极寻求金融发展趋同的有利因素。     

中国省域保险业发展影响因素的空间计量分析

运用空间常系数空间滞后模型、空间误差模型和空间变系数的地理加权回归模型,对中国省域的保险业发展进行空间计量分析。研究结果表明：中国31个省域的保险业发展在空间分布上具有明显的空间正自相关关系,各省域保险业的发展主要受到地区经济发展水平、产业结构、人口抚养状况、教育水平和社会保障水平的影响,并且这些因素对保险业的发展都有着显著的正向作用。     

顾客体验价值影响因素研究——以星巴克咖啡为例

随着社会经济的发展,消费水平和层次也相应提高。消费者需要的不仅仅是产品或服务,更需要通过对产品或服务的消费来获得充满感性的享受和难以忘怀的愉悦。以顾客体验价值理论为基础,以星巴克咖啡为实例,利用因子分析、相关分析和多元回归分析了影响顾客体验价值的因素,结果表明:影响顾客体验价值的因素有情境因素,也有顾客因素,而顾客的体验价值既包括心理体验价值,也包括感官体验价值。     

Efficient design of experiments in the Monod model

Summary. Estimation and experimental design in a non-linear regression model that is used in microbiology are studied. The Monod model is defined implicitly by a differential equation and has numerous applications in microbial growth kinetics, water research, pharmacokinetics and plant physiology. It is proved that least squares estimates are asymptotically unbiased and normally distributed. The asymptotic covariance matrix of the estimator is the basis for the construction of efficient designs of experiments. In particular locally D -, E - and c -optimal designs are determined and their properties are studied theoretically and by simulation. If certain intervals for the non-linear parameters can be specified, locally optimal designs can be constructed which are robust with respect to a misspecification of the initial parameters and which allow efficient parameter estimation. Parameter variances can be decreased by a factor of 2 by simply sampling at optimal times during the experiment.     

基于生存分析法的医疗电子病历系统采纳与扩散研究

医疗电子病历系统作为中国医疗信息化建设的核心,关注其采纳与扩散机理,对推进医疗卫生信息化建设,以及实现有意义地使用具有重要的理论和实践意义。基于云南省222家医院信息化建设的调研数据,运用半参数生存分析法即Cox回归模型探讨医院采纳电子病历系统的影响因素及扩散机理。研究表明,教学状态、医院规模和建院时间均为有利因素,即这三个因素积极促进医院采纳电子病历系统;时间×规模为不利因素,即它对医院采纳电子病历系统有负向影响;地理位置在分层和删减样本的模型分析中均表现为不利因素;医院等级影响并不明显。     

Hierarchical likelihood approach to non-Gaussian factor analysis

Factor models, structural equation models (SEMs) and random-effect models share the common feature that they assume latent or unobserved random variables. Factor models and SEMs allow well developed procedures for a rich class of covariance models with many parameters, while random-effect models allow well developed procedures for non-normal models including heavy-tailed distributions for responses and random effects. In this paper, we show how these two developments can be combined to result in an extremely rich class of models, which can be beneficial to both areas. A new fitting procedures for binary factor models and a robust estimation approach for continuous factor models are proposed.     

Correcting for regression dilution bias: comparison of methods for a single predictor variable

In an epidemiological study the regression slope between a response and predictor variable is underestimated when the predictor variable is measured imprecisely. Repeat measurements of the predictor in individuals in a subset of the study or in a separate study can be used to estimate a multiplicative factor to correct for this 'regression dilution bias'. In applied statistics publications various methods have been used to estimate this correction factor. Here we compare six different estimation methods and explain how they fall into two categories, namely regression and correlation-based methods. We provide new asymptotic variance formulae for the optimal correction factors in each category, when these are estimated from the repeat measurements subset alone, and show analytically and by simulation that the correlation method of choice gives uniformly lower variance. The simulations also show that, when the correction factor is not much greater than 1, this correlation method gives a correction factor which is closer to the true value than that from the best regression method on up to 80% of occasions. We also provide a variance formula for a modified correlation method which uses the standard deviation of the predictor variable in the main study; this shows further improved performance provided that the correction factor is not too extreme. A confidence interval for a corrected regression slope in an epidemiological study should reflect the imprecision of both the uncorrected slope and the estimated correction factor. We provide formulae for this and show that, particularly when the correction factor is large and the size of the subset of repeat measures is small, the effect of allowing for imprecision in the estimated correction factor can be substantial.     

Factor analysis regression

The paper describes two regression models—principal components and maximum-likelihood factor analysis—which may be used when the stochastic predictor varibles are highly intereorrelated and/or contain measurement error. The two problems can occur jointly, for example in social-survey data where the true (but unobserved) covariance matrix can be singular. Departure from singularity of the sample dispersion matrix is then due to measurement error. We first consider the more elementary principal components regression model, where it is shown that it can be derived as a special case of (i) canonical correlation, and (ii) restricted least squares. The second part consists of the more general maximum-likelihood factor-analysis regression model, which is derived from the generalized inverse of the product of two singular matrices. Also, it is proved that factor-analysis regression can be considered as an instrumental variables estimator and therefore does not depend on whether factors have been “properly” identified in terms of substantive behaviour. Consequently the additional task of rotating factors to “simple structure” does not arise.     

Latent Variable Modelling: A Survey*

Abstract. Latent variable modelling has gradually become an integral part of mainstream statistics and is currently used for a multitude of applications in different subject areas. Examples of ‘traditional’ latent variable models include latent class models, item–response models, common factor models, structural equation models, mixed or random effects models and covariate measurement error models. Although latent variables have widely different interpretations in different settings, the models have a very similar mathematical structure. This has been the impetus for the formulation of general modelling frameworks which accommodate a wide range of models. Recent developments include multilevel structural equation models with both continuous and discrete latent variables, multiprocess models and nonlinear latent variable models.     

Place of living and health inequality: a study for elderly Italians

The aim of this study is to explore if the context matters in explaining socioeconomic inequality in the self-rated health of Italian elderly people. Our hypothesis is that health status perception is associated with existing huge imbalances among Italian areas. A multilevel approach is applied to account for the natural hierarchical structure, as individuals nested in geographical regions. Multilevel logistic regression models are performed including both individual and contextual variables, using data from 2005 Italian Health survey. We prove that individual factors (compositional effect), even representing the most important correlates of health, do not completely explain intra-regional heterogeneity, confirming the existence of an autonomous contextual effect. These territorial differences are present among both Regions and large areas, two geographical aggregations relevant in the domain of health. Moreover, for some Regions, the account for contextual factors explains variations in perceived health, leading to an overthrow of the initial situation: these Regions perform better than expected in the field of health. For other Regions, the contextual elements introduced do not catch the milieu heterogeneity. In this regard, we expect, and solicit, a major effort toward data availability, qualitatively and quantitatively, that might help in explaining residual territorial heterogeneity in health perception, a fundamental starting point for targeting specific policy interventions.     

A Bayesian ordinal logistic regression model to correct for interobserver measurement error in a geographical oral health study

Summary.  We present an approach for correcting for interobserver measurement error in an ordinal logistic regression model taking into account also the variability of the estimated correction terms. The different scoring behaviour of the 16 examiners complicated the identification of a geographical trend in a recent study on caries experience in Flemish children (Belgium) who were 7 years old. Since the measurement error is on the response the factor 'examiner' could be included in the regression model to correct for its confounding effect. However, controlling for examiner largely removed the geographical east–west trend. Instead, we suggest a (Bayesian) ordinal logistic model which corrects for the scoring error (compared with a gold standard) using a calibration data set. The marginal posterior distribution of the regression parameters of interest is obtained by integrating out the correction terms pertaining to the calibration data set. This is done by processing two Markov chains sequentially, whereby one Markov chain samples the correction terms. The sampled correction term is imputed in the Markov chain pertaining to the regression parameters. The model was fitted to the oral health data of the Signal–Tandmobiel^® study. A WinBUGS program was written to perform the analysis.