中国省域碳排放的空间特征及影响因素

通过核密度分布和莫兰指数对中国2000—2015年30省份碳排放强度的动态趋势及集聚特征进行测度,并利用空间杜宾模型对其主要影响因素进行分析。结果显示:（1）中国30省份碳排放强度呈下降趋势,新常态以来低碳步伐加快;（2）碳排放强度的空间集聚性具有高水平集中、低水平集聚特征,空间溢出效应不断增强;（3）本省经济规模、产业结构对本省碳排放强度具有显著的正向影响,专利产出具有显著的负向影响;相邻省份的外商投资规模及能源消费结构变化对本省碳排放具有显著的空间溢出作用。因此,未来中国加快产业结构调整幅度、优化相邻省份间的产业空间布局以及大力发展绿色技术进步是中国促进区域低碳转型的主要方向,同时生态城镇化以及继续改善外商直接投资质量也是减排潜力因子,省域间的减排空间溢出效果不容忽视。     

Infill asymptotics for adaptive kernel estimators of spatial intensity

We apply the Abramson principle to define adaptive kernel estimators for the intensity function of a spatial point process. We derive asymptotic expansions for the bias and variance under the regime that n independent copies of a simple point process in Euclidean space are superposed. The method is illustrated by means of a simple example and applied to tornado data.     

A Hellinger distance approach to MCMC diagnostics

Bayesian analysis often requires the researcher to employ Markov Chain Monte Carlo (MCMC) techniques to draw samples from a posterior distribution which in turn is used to make inferences. Currently, several approaches to determine convergence of the chain as well as sensitivities of the resulting inferences have been developed. This work develops a Hellinger distance approach to MCMC diagnostics. An approximation to the Hellinger distance between two distributions f and g based on sampling is introduced. This approximation is studied via simulation to determine the accuracy. A criterion for using this Hellinger distance for determining chain convergence is proposed as well as a criterion for sensitivity studies. These criteria are illustrated using a dataset concerning the Anguilla australis, an eel native to New Zealand.     

A modified test for testing exponentiality using transformed data

In this article, we present a test for testing uniformity. Based on the test, we provide a test for testing exponentiality. Empirical critical values for both the tests are computed. Both the tests are compared with the tests proposed by Noughabi and Arghami [H. Alizadeh Noughabi, and N.R. Arghami, Testing exponentiality using transformed data, J. Statist. Comput. Simul. 81 (4) (2011), pp. 511–516] using simulation experiments for a wide class of alternatives. The tests possess attractive power properties.     

Asymptotic behaviour of binned kernel density estimators for locally non-stationary random fields

We investigate the asymptotic behaviour of binned kernel density estimators for dependent and locally non-stationary random fields converging to stationary random fields. We focus on the study of the bias and the asymptotic normality of the estimators. A simulation experiment conducted shows that both the kernel density estimator and the binned kernel density estimator have the same behavior and both estimate accurately the true density when the number of fields increases. We apply our results to the 2002 incidence rates of tuberculosis in the departments of France.     

A new algorithm for clustering based on kernel density estimation

In this paper, we present an algorithm for clustering based on univariate kernel density estimation, named ClusterKDE. It consists of an iterative procedure that in each step a new cluster is obtained by minimizing a smooth kernel function. Although in our applications we have used the univariate Gaussian kernel, any smooth kernel function can be used. The proposed algorithm has the advantage of not requiring a priori the number of cluster. Furthermore, the ClusterKDE algorithm is very simple, easy to implement, well-defined and stops in a finite number of steps, namely, it always converges independently of the initial point. We also illustrate our findings by numerical experiments which are obtained when our algorithm is implemented in the software Matlab and applied to practical applications. The results indicate that the ClusterKDE algorithm is competitive and fast when compared with the well-known Clusterdata and K-means algorithms, used by Matlab to clustering data.     

Estimation and simulation of conditional hazard function in the quasi-associated framework when the observations are linked via a functional single-index structure

We consider the estimation of the conditional hazard function of a scalar response variable Y given a Hilbertian random variable X when the observations are linked via a single-index structure in the quasi-associated framework. We establish the pointwise almost complete convergence and the uniform almost complete convergence (with the rate) of the estimate of this model. A simulation is given to illustrate the good behavior in the practice of our methodology.     

论分享价值原则的文化战略学意义--十八届三中全会《决定》的理论解读

在“五位一体”框架中，探讨支撑国家整体发展战略的基本价值原则，乃是当代中国化马克思主义文化战略学的重要课题之一。中共十八届三中全会《决定》强调“让一切劳动、知识、技术、管理、资本的活力竞相迸发”，要做到这一点，在市场之内的初次分配中，必须让“资本-劳动”分享利润，与此相对应的是一种与“平等”等密切相关的“分享”价值原则。《决定》有关“多渠道增加居民财产性收入”“保障农民公平分享土地增值收益”等重大举措，同样是在市场之内贯彻分享价值原则，而以民生为主的社会建设、以公共服务为主的文化事业则是在市场之外推行分享价值原则的主要方式。全面推行分享价值原则，既有利于坚定地推进新型市场化改革，同时也有利于保障平等、促进和谐、拓展自由，对内可以凝聚社会共识，激发国民创造力，对外则有助于在价值制高点上展现社会主义中国的文化魅力和感召力。     

On Modeling Wood Formation Using Parametric and Semiparametric Regressions for Count Data

Understanding how wood develops has become an important problematic of plant sciences. However, studying wood formation requires the acquisition of count data difficult to interpret. Here, the annual wood formation dynamics of a conifer tree species were modeled using generalized linear and additive models (GLM and GAM); GAM for location, scale, and shape (GAMLSS); a discrete semiparametric kernel regression for count data. The performance of models is evaluated using bootstrap methods. GLM was useful to describe the wood formation general pattern but had a lack of fitting, while GAM, GAMLSS, and kernel regression had a higher sensibility to short-term variations.