Adjusting the 1980 census of population and housing

"In 1980, several cities and states sued the U.S. Census Bureau to correct census results. This correction would adjust for the differential undercounting of Blacks and Hispanics, especially in cities. In this article, the authors, each of whom testified for New York City and State in their joint lawsuit against the Census Bureau, describe the likely pattern of the undercount and present a method to adjust for it." The authors describe available methods for data adjustment and introduce a regression-based composite method of adjustment, which is used to estimate the undercounts for 66 areas. "As expected, we find that the highest undercount rates are in large cities, and the lowest are in states and state remainders with small percentages of Blacks and Hispanics. Next, we analyze how sensitive our estimates are to changes in data and modeling assumptions. We find that these changes do not affect the estimates very much. Our conclusion is that regardless of whether we use one of the simple methods or the composite method and regardless of how we vary the assumptions of the composite method, an adjustment reliably reduces population shares in states with few minorities and increases the shares of large cities."     

我国城市规模发展水平分析与比较研究

城市规模发展是城市化研究的重要问题之一。最早在古希腊时的亚里士多德就已经开始注意并研究了城市规模的发展问题。 2 0世纪以来 ,由于世界城市化发展迅速 ,不仅城市数量在急剧增加 ,城市规模也在不断扩大 ,于是城市规模发展水平问题的研究就被日益重视起来。在我国随着城市化速度的加快 ,城市规模发展水平问题的研究也被提到议事日程上来。城市发展规模究竟多大为适度 ,命理水平应该如何 ,它与城市规模效率、城市规模分布、城市可续发展能力等有着密切关系。为此 ,本文将对此进行实证分析 ,以便为我国制定城市发展政策 ,探讨城市发展规模…     

基于分位数回归的FDI城市分布影响因素研究

中国的外商直接投资(FDI)主要集中在部分城市,其在城市间的分布呈现出均值左偏的特征。针对FDI在城市间分布不具备正态性且存在较多离群点的问题,运用一般分位数回归方法分析1994-2012年影响FDI在中国287个城市分布的影响因素。研究表明,劳动力成本、市场规模和潜力、制度质量、人力资本、基础设施和当地竞争环境都是影响FDI城市分布的重要因素。同时,各因素对FDI影响程度随FDI城市分位的变化而变化。市场规模与潜力、政府效率、人力资本对FDI的影响随分位点增高有明显下降趋势,而基础设施对FDI的促进作用随分位点增高而上升。     

陕西省地缘城市间经济合作分析

在地缘经济一体化发展背景下,陕西省主要城市如何充分利用地缘优势进行区域经济合作来推动陕西经济发展就成为亟待解决的问题。运用欧氏距离法对陕西各城市地缘经济关系进行数量测度和类型判别,提出通过增强地缘城市间经济的互补性、发挥中心城市的"极化"和"扩散"效应以及提供畅通的合作渠道等途径来促进经济合作。     

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。     

城市福利水平结构性差异的测度及其对人口迁移的影响

文章在将城市福利分为户籍福利和非户籍福利的基础上,基于熵值法构建了城市福利水平评价指标体系,对全国288个地级以上城市的福利水平进行了测算。结果发现,户籍福利和非户籍福利都随城市规模的增大而增大,不同规模城市的福利差距很大,少数特大城市的非户籍福利水平高于其他城市户籍福利和非户籍福利的总和。城市福利的这种结构性差异对人口迁移产生了重要影响:农业转移人口宁愿到特大城市作非户籍居民,也不愿意到中小城市作户籍居民,特大城市即使严控户籍,也无法改变人口膨胀的态势。因此,特大城市人口疏解不应靠户籍管制,而应致力于缩小不同规模城市之间的福利水平差距。     

一种协整时间序列的动态因果效应估计与推断方法

为了进一步规避经典面板数据模型因果推断方法中对照样本异质性的问题,以及中断时间序列方法可能存在的“伪回归”问题和“二阶矩平稳”等条件的限制,本文借助时间序列的协整理论,并结合误差修正模型和结构突变检验方法提出了一种基于协整时间序列的实验设计方法,扩展了政策因果效应估计与推断方法。该方法将传统的趋势平稳过程中断时间序列扩展到随机趋势过程,不仅可以推断和估计处理效应,而且解决了政策效应的暂时性和持续性识别问题;另外,协整时间序列的因果效应推断方法更适用于不存在有效对照样本的政策因果效应分析。此外,本文评估了上海市和重庆市的房地产税试点政策,研究发现,长期来看试点政策对两市的商品房价格增长具有抑制作用,但政策效应表现出显著的差异性,上海市的试点政策对抑制房价上涨的力度大、起效快速,但重庆市的房产税政策存在较长期的时滞。     

SPARSE LOGISTIC PRINCIPAL COMPONENTS ANALYSIS FOR BINARY DATA

We develop a new principal components analysis (PCA) type dimension reduction method for binary data. Different from the standard PCA which is defined on the observed data, the proposed PCA is defined on the logit transform of the success probabilities of the binary observations. Sparsity is introduced to the principal component (PC) loading vectors for enhanced interpretability and more stable extraction of the principal components. Our sparse PCA is formulated as solving an optimization problem with a criterion function motivated from penalized Bernoulli likelihood. A Majorization-Minimization algorithm is developed to efficiently solve the optimization problem. The effectiveness of the proposed sparse logistic PCA method is illustrated by application to a single nucleotide polymorphism data set and a simulation study.     

地方资源禀赋、制度环境与地方债发行

我国地方政府债务水平的高速增长引发了各界的警惕,而地方政府的发债动机、发债能力更是饱受质疑。本文利用2004-2012年我国地级市的发债数据,对政府发债动机及发债能力进行研究。我们发现,地方政府的投资冲动越强,地方政府发行债券的可能性越高;城市的财政赤字情况越严重,则地方政府发行债券的机会越低,而城市的经济规模、经济增速则对城市发债能力产生正面影响。在此基础上,我们进一步研究制度环境对城市发债行为的影响。研究表明,金融成熟度对城市特征产生了“放大”效应,发债双方的关系更加遵从“市场规则”;而“关系成本”的存在则打破了“市场规则”对地方政府的约束。我们的研究深化了对地方政府发债行为及地方政府债务风险的理解。     

MULTIPLE COMPARISONS OF LOG-LIKELIHOODS AND COMBINING NONNESTED MODELS WITH APPLICATIONS TO PHYLOGENETIC TREE SELECTION

We consider multiple comparisons of log-likelihood's to take account of the multiplicity of testings in selection of nonnested models. A resampling version of the Gupta procedure for the selection problem is used to obtain a set of good models, which are not significantly worse than the maximum likelihood model; i.e., a confidence set of models. Our method is to test which model is better than the other, while the object of the classical testing methods is to find the correct model. Thus the null hypotheses behind these two approaches are very different. Our method and the other commonly used approaches, such as the approximate Bayesian posterior, the bootstrap selection probability, and the LR test against the full model, are applied to the selection of molecular phylogenetic tree of mammal species. Tree selection is a version of the model-based clustering, which is an example of nonnested model selection. It is shown that the structure of the tree selection problem is equivalent to that of the variable selection problem of the multiple regression with some constraints on the combinations of the variables. It turns out that the LR test rejects all the possible trees because of the misspecification of the models, whereas our method gives a reasonable confidence set. For a better understanding of the uncertainty in the selection, we combine the maximum likelihood estimates (MLE's) of the trees to obtain the full model that includes the trees as the submodels by using a linear approximation of the parametric models. The MLE of the phylogeny is then represented as a network of species rather than a tree. A geometrical interpretation of the problem is also discussed.     

Feasible algorithm for linear mixed model for massive data

This article studies computation problem in the context of estimating parameters of linear mixed model for massive data. Our algorithms combine the factored spectrally transformed linear mixed model method with a sequential singular value decomposition calculation algorithm. This combination solves the operation limitation of the method and also makes this algorithm feasible to big dataset, especially when the data has a tall and thin design matrix. Our simulation studies show that our algorithms make the calculation of linear mixed model feasible for massive data on ordinary desktop and have same estimating accuracy with the method based on the whole data.     

Local polynomial regression and simulation–extrapolation

Summary.  The paper introduces a new local polynomial estimator and develops supporting asymptotic theory for nonparametric regression in the presence of covariate measurement error. We address the measurement error with Cook and Stefanski's simulation–extrapolation (SIMEX) algorithm. Our method improves on previous local polynomial estimators for this problem by using a bandwidth selection procedure that addresses SIMEX's particular estimation method and considers higher degree local polynomial estimators. We illustrate the accuracy of our asymptotic expressions with a Monte Carlo study, compare our method with other estimators with a second set of Monte Carlo simulations and apply our method to a data set from nutritional epidemiology. SIMEX was originally developed for parametric models. Although SIMEX is, in principle, applicable to nonparametric models, a serious problem arises with SIMEX in nonparametric situations. The problem is that smoothing parameter selectors that are developed for data without measurement error are no longer appropriate and can result in considerable undersmoothing. We believe that this is the first paper to address this difficulty.     

Gaussian Regularized Sliced Inverse Regression

Sliced Inverse Regression (SIR) is an effective method for dimension reduction in high-dimensional regression problems. The original method, however, requires the inversion of the predictors covariance matrix. In case of collinearity between these predictors or small sample sizes compared to the dimension, the inversion is not possible and a regularization technique has to be used. Our approach is based on a Fisher Lecture given by R.D. Cook where it is shown that SIR axes can be interpreted as solutions of an inverse regression problem. We propose to introduce a Gaussian prior distribution on the unknown parameters of the inverse regression problem in order to regularize their estimation. We show that some existing SIR regularizations can enter our framework, which permits a global understanding of these methods. Three new priors are proposed leading to new regularizations of the SIR method. A comparison on simulated data as well as an application to the estimation of Mars surface physical properties from hyperspectral images are provided.     

Estimation of the variance of an adaptive estimator by bootstrapping

In this paper we propose a bootstrap-based method to estimate the standard error of adaptive estimators. We apply it in the standard problem of location estimation discussed in Randies and Hogg (1973) and in Hogg and Lenth (1984). Our adaptive estimator is based on a choice between the mean the 35% trimmed mean and the median. Finally, we carry out a simulation study to see how well the proposed method performs in small and moderate sample sizes.     

中国城市住宅资本存量及资本回报率的估算

基于永续盘存法,本文创新性地估算了1997-2016年285个地级及以上城市的住宅资本存量,在此基础上,我们细致讨论了城市层面住宅资本存量的基本特征,并估计了住宅资本的回报率。估算结果表明：第一,从总体城市来看,住宅资本存量的年均增长率明显高于GDP的年均涨幅。第二,各地区和各分线城市的人均住宅资本存量均呈不断上升趋势;其中,西部地区和二线城市的人均住宅资本存量增速更为迅猛。第三,近年来,除一线城市的住宅资本回报率有所上升以外,其他地区的住宅资本回报率均表现出大幅下降的趋势,即住宅投资效率在不断下降。但分地区、分线城市住宅资本回报率的变化呈现收敛特征。综上,应该警惕住宅资本存量的快速增长可能对投资效率带来的负面影响。     

城市医生资源与新冠肺炎防控效率

本文利用非参数估计法考察了我国城市层面的新冠肺炎病情防控效率,讨论了医生资源对防控效率的影响。结果表明,城市疫情防控措施显著降低了每日新增病例,但存在明显地区异质性,城市医生资源差异是解释防控效率异质性的重要因素。医生资源对防控效率的影响具有非线性特征,在资源紧缺城市边际促进作用更大。包括病床和医院数量在内的医疗物质投入没有产生边际影响,表明医疗物质资源还未达到约束状态。进一步分析医生职业结构发现,临床医生的作用最为显著。地区应急响应启动越早,医生资源的作用越大,因此政府相关政策是医生资源作用的有力保障。新冠肺炎疫情一定程度上暴露了我国医生资源短板,需要多措并举有效提升城市医生资源供给。     

Smoothed Extreme Value Estimators of Non-Uniform Point Processes Boundaries with Application to Star-Shaped Supports Estimation

We address the problem of estimating the edge of a bounded set in ? ^d given a random set of points drawn from the interior. Our method is based on a transformation of estimators dedicated to uniform point processes and obtained by smoothing some of its bias corrected extreme points. An application to the estimation of star-shaped supports is presented.     

A parametric model for heterogeneity in paired Poisson counts

We present a model for data in the form of matched pairs of counts. Our work is motivated by a problem in fission-track analysis, where the determination of a crystal's age is based on the ratio of counts of spontaneous and induced tracks. It is often reasonable to assume that the counts follow a Poisson distribution, but typically they are overdispersed and there exists a positive correlation between the numbers of spontaneous and induced tracks in the same crystal. We propose a model that allows for both overdispersion and correlation by assuming that the mean densities follow a bivariate Wishart distribution. Our model is quite general, having the usual negative-binomial and Poisson models as special cases. We propose a maximum-likelihood estimation method based on a stochastic implementation of the EM algorithm, and we derive the asymptotic standard errors of the parameter estimates. We illustrate the method with a data set of fission-track counts in matched areas of zircon crystals.     

Model-based adaptive spatial sampling for occurrence map construction

In many environmental management problems, the construction of occurrence maps of species of interest is a prerequisite to their effective management. However, the construction of occurrence maps is a challenging problem because observations are often costly to obtain (thus incomplete) and noisy (thus imperfect). It is therefore critical to develop tools for designing efficient spatial sampling strategies and for addressing data uncertainty. Adaptive sampling strategies are known to be more efficient than non-adaptive strategies. Here, we develop a model-based adaptive spatial sampling method for the construction of occurrence maps. We apply the method to estimate the occurrence of one of the world’s worst invasive species, the red imported fire ant, in and around the city of Brisbane, Australia. Our contribution is threefold: (i) a model of uncertainty about invasion maps using the classical image analysis probabilistic framework of Hidden Markov Random Fields (HMRF), (ii) an original exact method for optimal spatial sampling with HMRF and approximate solution algorithms for this problem, both in the static and adaptive sampling cases, (iii) an empirical evaluation of these methods on simulated problems inspired by the fire ants case study. Our analysis demonstrates that the adaptive strategy can lead to substantial improvement in occurrence mapping.     

Bayesian deconvolution of oil well test data using Gaussian processes

We use Bayesian methods to infer an unobserved function that is convolved with a known kernel. Our method is based on the assumption that the function of interest is a Gaussian process and, assuming a particular correlation structure, the resulting convolution is also a Gaussian process. This fact is used to obtain inferences regarding the unobserved process, effectively providing a deconvolution method. We apply the methodology to the problem of estimating the parameters of an oil reservoir from well-test pressure data. Here, the unknown process describes the structure of the well. Applications to data from Mexican oil wells show very accurate results.