新时期南沙建设“城市副中心”发展战略思考

城市在一定时期应对接国家战略,集全市之力,重点建设一个新区域,尽快形成规模,带动全市经济社会转型升级。城市新区功能定位应该是综合性的,多元化功能才能形成人气,相互促进;新区建设不仅仅是建几条地铁,更重要的是要使新城区成为轨道交通枢纽,这样才能汇聚人流;新区不但要发展生产性第三产业,而且要大力发展生活性第三产业;新区还要形成独立的生产生活体系,产城融合,职住平衡。     

基于多源遥感的城市生态用地识别及生态风险评价

随着城市化进程的不断推进、建设用地不断扩张,城市生态空间正处于剧烈变化中。大量具有生态价值和服务功能的土地不断被侵占,以满足暂时的城市建设需求。但从长远来看,这些生态用地的"消失",将不可避免的影响区域生态平衡,给区域经济的健康发展埋下隐患。广州作为华南的中心城市,改革开放以来,大量外来人口涌入,土地利用变化频繁,生态环境的保护面临着巨大压力。因此,本文基于多源遥感数据,利用监督分类法与综合指数法相综合的复合提取方案对2016年广州生态用地进行空间识别,掌握广州市生态用地存量现状。同时,借助生态风险小区和生态风险等景观指数对广州进行了生态风险评估,结果显示,广州市生态风险呈现明显的多核圈层结构,由多核心的中心向外部逐渐降低。     

Sociopolitical and Cultural Correlates of Internalized Homonegativity in Gay and Bisexual Men: Findings from a Global Study

Objectives: Based on the premise that internalized homonegativity (IH) is a product of the incorporation of environmental heterosexism, the authors examined the influence of sociopolitical and individual influences on IH. Methods: The cross-sectional study consisted of 109,382 gay and bisexual men across 77 countries. Results: Variables at the (European) country-level that were associated with higher levels of IH included lack of laws recognizing same-sex relationships and perceived and actual negative gay-related public opinion about homosexuals. Individual-level variables significantly associated with IH were public opinion about homosexuals and exposure to gay-related victimization/discrimination. Conclusions: An improved sociopolitical climate for LGB individuals is needed.     

A Comparison of Correlation Structure Selection Penalties for Generalized Estimating Equations

Correlated data are commonly analyzed using models constructed using population-averaged generalized estimating equations (GEEs). The specification of a population-averaged GEE model includes selection of a structure describing the correlation of repeated measures. Accurate specification of this structure can improve efficiency, whereas the finite-sample estimation of nuisance correlation parameters can inflate the variances of regression parameter estimates. Therefore, correlation structure selection criteria should penalize, or account for, correlation parameter estimation. In this article, we compare recently proposed penalties in terms of their impacts on correlation structure selection and regression parameter estimation, and give practical considerations for data analysts. Supplementary materials for this article are available online.     

Quantile regression for robust estimation and variable selection in partially linear varying-coefficient models

In this paper, we develop a new estimation procedure based on quantile regression for semiparametric partially linear varying-coefficient models. The proposed estimation approach is empirically shown to be much more efficient than the popular least squares estimation method for non-normal error distributions, and almost not lose any efficiency for normal errors. Asymptotic normalities of the proposed estimators for both the parametric and nonparametric parts are established. To achieve sparsity when there exist irrelevant variables in the model, two variable selection procedures based on adaptive penalty are developed to select important parametric covariates as well as significant nonparametric functions. Moreover, both these two variable selection procedures are demonstrated to enjoy the oracle property under some regularity conditions. Some Monte Carlo simulations are conducted to assess the finite sample performance of the proposed estimators, and a real-data example is used to illustrate the application of the proposed methods.     

A varying coefficient approach to estimating hedonic housing price functions and their quantiles

The varying coefficient (VC) model introduced by Hastie and Tibshirani [26 T. Hastie and R. Tibshirani, Varying-coefficient models, J. R. Statist. Soc. (Ser. B) 55 (1993), pp. 757–796.[Web of Science ®] , [Google Scholar]] is arguably one of the most remarkable recent developments in nonparametric regression theory. The VC model is an extension of the ordinary regression model where the coefficients are allowed to vary as smooth functions of an effect modifier possibly different from the regressors. The VC model reduces the modelling bias with its unique structure while also avoiding the ‘curse of dimensionality’ problem. While the VC model has been applied widely in a variety of disciplines, its application in economics has been minimal. The central goal of this paper is to apply VC modelling to the estimation of a hedonic house price function using data from Hong Kong, one of the world's most buoyant real estate markets. We demonstrate the advantages of the VC approach over traditional parametric and semi-parametric regressions in the face of a large number of regressors. We further combine VC modelling with quantile regression to examine the heterogeneity of the marginal effects of attributes across the distribution of housing prices.     

Tests for Structural Changes in Time Series of Counts

We propose methods for detecting structural changes in time series with discrete‐valued observations. The detector statistics come in familiar L2‐type formulations incorporating the empirical probability generating function. Special emphasis is given to the popular models of integer autoregression and Poisson autoregression. For both models, we study mainly structural changes due to a change in distribution, but we also comment for the classical problem of parameter change. The asymptotic properties of the proposed test statistics are studied under the null hypothesis as well as under alternatives. A Monte Carlo power study on bootstrap versions of the new methods is also included along with a real data example.     

Robust modal estimation and variable selection for single-index varying-coefficient models

In this article, we present a new efficient iteration estimation approach based on local modal regression for single-index varying-coefficient models. The resulted estimators are shown to be robust with regardless of outliers and error distributions. The asymptotic properties of the estimators are established under some regularity conditions and a practical modified EM algorithm is proposed for the new method. Moreover, to achieve sparse estimator when there exists irrelevant variables in the index parameters, a variable selection procedure based on SCAD penalty is developed to select significant parametric covariates and the well-known oracle properties are also derived. Finally, some numerical examples with various distributed errors and a real data analysis are conducted to illustrate the validity and feasibility of our proposed method.     

Smoothing the difference-based estimates of variance using variational mode decomposition

We propose a variational mode decomposition approach to estimate the variance function in a nonparametric heteroscedastic fixed design regression model. A data-driven estimator is constructed by applying variational mode decomposition technique to the difference-based initial estimates. The numerical results show that the proposed estimator performs better than the existing variance estimation procedures in the mean square sense.