影响我国都市圈经济总量的因素分析——基于12大都市圈的面板数据

借助当前我国发展较成熟的12个都市圈的相关数据，通过建立多元回归模型，考察分析都市圈经济发展背后的影响因素，研究表明：都市圈中心城市辐射力、都市圈联系力和设施水平等因素对经济发展较快的都市圈经济总量有显著影响；经济发展较慢的都市圈的经济总量则主要受到都市圈消费能力和教育水平的影响，还受到一些都市圈软实力因素的影响。     

河南经济增长的统计分析

以河南省1978年以来历年的生产总值为研究对象,从中原崛起的背景出发,简要说明了河南经济发展的现状及特点,分析了河南省生产总值的构成情况,通过与东部沿海发达省份进行比较,发现问题,针对分析出来的结果和存在的问题,找出原因并提出了推动河南经济增长的一些对策及建议。     

Bringing Japanese Continuous Improvement Approaches to U.S. Manufacturing: The Roles of Process Orientation and Communications*

Continuous improvement (CI) has played a key role in Japan's quality management. U.S. companies have begun to adopt CI in recent years. This paper studies the implementation of CI in seven U.S. manufacturing companies. We view values as the core concept of culture that impacts CI effectiveness. Using both qualitative and quantitative data with a sample of seven companies, we observe empirically a relationship between process-oriented values and CI effectiveness. We also find that communications involving workers is strongly associated with CI effectiveness. Finally, we find an association between process orientation and communication frequency, and argue that communications act as an intervening variable between process orientation and effectiveness.     

我国制造业集聚变迁与全要素生产率增长研究

本文选取我国29个省市区和17个制造行业1990～2009年的面板数据,对相对专业化与相对多样化集聚度进行了计算及变迁分析,利用Malmquist指数测算了全要素生产率的增长,并分析了产业集聚变迁对全要素生产率的影响。结果表明:我国东部地区呈现多样化与专业化集聚均衡发展的特征,而中西部地区则具有相对较高的专业化集聚水平,呈现产业结构单一化与非均衡化;东部地区全要素生产率增长较快,中西部地区相对滞后;相对专业化与多样化集聚对东部、中部与西部地区的影响存在地区差异。基于此,本文提出了加快产业结构调整、实施均衡的区域发展政策和提高经济组织制度绩效的政策建议。     

上海城市人口总量控制与结构优化研究

上海的人口总量和结构一直与其独特的工业化、城镇化进程密不可分。工业化带动人口发展,人口发展推动城市发展。改革开放以来,上海正处于第四波工业化时期,人口增长较快。在人口结构上,户籍人口老龄化,而外来人口具有就近流动、城城流动、定居化特点,且教育水平逐年提高。从承载力上看,上海人口总量合理,但人口分布的不合理影响了人口结构的发展、城市社会的融合,带来了核心区人口过密、产城融合不畅的问题,以及偏好"人脑"、排斥"人手"的问题。面对人口老龄化和可能的人口逆流,上海应稳定人口总量,建设整体推进的多中心发展人口分布格局,完善户籍制度改革,破解"新二元结构",推动社会保障和公共服务均等化,同时结合政府改革,完善相关配套改革措施。     

On distribution-weighted partial least squares with diverging number of highly correlated predictors

Summary.  Because highly correlated data arise from many scientific fields, we investigate parameter estimation in a semiparametric regression model with diverging number of predictors that are highly correlated. For this, we first develop a distribution-weighted least squares estimator that can recover directions in the central subspace, then use the distribution-weighted least squares estimator as a seed vector and project it onto a Krylov space by partial least squares to avoid computing the inverse of the covariance of predictors. Thus, distrbution-weighted partial least squares can handle the cases with high dimensional and highly correlated predictors. Furthermore, we also suggest an iterative algorithm for obtaining a better initial value before implementing partial least squares. For theoretical investigation, we obtain strong consistency and asymptotic normality when the dimension p of predictors is of convergence rate O { n ^1/2/ log ( n )} and o ( n ^1/3) respectively where n is the sample size. When there are no other constraints on the covariance of predictors, the rates n ^1/2 and n ^1/3 are optimal. We also propose a Bayesian information criterion type of criterion to estimate the dimension of the Krylov space in the partial least squares procedure. Illustrative examples with a real data set and comprehensive simulations demonstrate that the method is robust to non-ellipticity and works well even in 'small n –large p ' problems.     

Bayesian inference for the parameters of Weibull distribution under progressive Type-I interval censored data with beta-binomial removals

This article considers the problem of estimating the parameters of Weibull distribution under progressive Type-I interval censoring scheme with beta-binomial removals. Classical as well as the Bayesian procedures for the estimation of unknown model parameters have been developed. The Bayes estimators are obtained under SELF and GELF using MCMC technique. The performance of the estimators, has been discussed in terms of their MSEs. Further, expression for the expected number of total failures has been obtained. A real dataset of the survival times for patients with plasma cell myeloma is used to illustrate the suitability of the proposed methodology.     

Comparison of methods for analyzing binary repeated measures data: A simulation-based study (comparison of methods for binary repeated measures)

In this study, some methods suggested for binary repeated measures, namely, Weighted Least Squares (WLS), Generalized Estimating Equations (GEE), and Generalized Linear Mixed Models (GLMM) are compared with respect to power, type 1 error, and properties of estimates. The results indicate that with adequate sample size, no missing data, the only covariate being time effect, and a relatively limited number of time points, the WLS method performs well. The GEE approach performs well only for large sample sizes. The GLMM method is satisfactory with respect to type I error, but its estimates have poorer properties than the other methods.     

Letter to the editor comments on Groparu-Cojocaru and Doray (2013)

Although estimating the five parameters of an unknown Generalized Normal Laplace (GNL) density by minimizing the distance between the empirical and true characteristic functions seems appealing, the approach cannot be advocated in practice. This conclusion is based on extensive numerical simulations in which a fast minimization procedure delivers deceiving estimators with values that are quite far away from the truth. These findings can be predicted by the very large values obtained for the true asymptotic variances of the estimators of the five parameters of the true GNL density.