基于非参数信息扩散模型的湖北水稻生产灾害风险评估

在小样本条件下估计灾害损失,非参数信息扩散模型相比一般的参数和非参数估计效果更好,因此本文利用湖北省1991—2007年县级水稻单产数据,基于非参数核密度估计的信息扩散方法来评估湖北水稻生产灾害风险。研究结果表明,各地区水稻灾害损失率的概率分布及各地区灾害风险的相对大小,与湖北洪涝和干旱灾害发生的时空分布及灾害程度较为吻合,可为湖北水稻县域产量保险风险区划研究提供支持。     

多元主体环境责任协同水平的地区差异与动态演进

基于中国30个省、自治区、直辖市（由于数据原因暂未包含西藏和港澳台地区）,构建多元主体环境责任协同水平指标体系,运用复合系统协同度模型,定量测算多元主体环境责任协同水平,并利用Dagum基尼系数和核密度估计方法分析其空间格局和动态演进过程。研究结果表明：中国各地多元主体环境责任协同水平整体偏低,呈现出逐年上升的趋势;环境责任协同水平空间分布的总体差异呈波动扩大趋势,地区间差距是总体差异的主要空间来源;环境责任协同水平的地区差异呈上升态势,东部、中部和西部三个地区的环境责任协同水平呈两极分化态势。     

京津冀人口集聚对能源消费的影响

京津冀作为我国重要的人口集聚区,其区域发展面临严重的能源和环境问题。首先,基于STIRPAT模型构建京津冀面板数据模型,研究1990—2017年京津冀人口密度、经济增长、产业结构和环境规制对能源消费的影响。其次,采用SVAR模型通过脉冲响应函数和方差分解,研究京津冀人口密度变动对能源消费的作用机理和动态影响关系。结果显示,京津冀三地的人口密度、人均实际GDP与能源消费之间存在正向影响关系,其中河北省影响系数最大;北京和天津第三产业占比与能源消费之间存在反向影响关系,河北省第三产业占比对能源消费的影响不显著;北京和天津的环境规制对能源消费影响不显著,河北省环境规制对能源消费具有正向影响;京津冀地区和京冀两地人口密度变动对能源消费的影响在短期呈现负向效应,但是在中长期京津冀地区和三地人口密度变动对能源消费的影响均先上升然后缓慢下降,人口集聚具有集约用能效应。相对于能源消费自身影响而言,京津冀人口密度变动对能源消费的贡献相对较小,其影响效应有限。最后,对京津冀人口流动和能源消费一体化协同发展提出对策建议。     

Deconvolution methods for non-parametric inference in two-level mixed models

Summary.  We develop a general non-parametric approach to the analysis of clustered data via random effects. Assuming only that the link function is known, the regression functions and the distributions of both cluster means and observation errors are treated non-parametrically. Our argument proceeds by viewing the observation error at the cluster mean level as though it were a measurement error in an errors-in-variables problem, and using a deconvolution argument to access the distribution of the cluster mean. A Fourier deconvolution approach could be used if the distribution of the error-in-variables were known. In practice it is unknown, of course, but it can be estimated from repeated measurements, and in this way deconvolution can be achieved in an approximate sense. This argument might be interpreted as implying that large numbers of replicates are necessary for each cluster mean distribution, but that is not so; we avoid this requirement by incorporating statistical smoothing over values of nearby explanatory variables. Empirical rules are developed for the choice of smoothing parameter. Numerical simulations, and an application to real data, demonstrate small sample performance for this package of methodology. We also develop theory establishing statistical consistency.     

Robust nonparametric estimation with missing data

In this paper, under a nonparametric regression model, we introduce two families of robust procedures to estimate the regression function when missing data occur in the response. The first proposal is based on a local M$M$-functional applied to the conditional distribution function estimate adapted to the presence of missing data. The second proposal imputes the missing responses using the local M$M$-smoother based on the observed sample and then estimates the regression function with the completed sample. We show that the robust procedures considered are consistent and asymptotically normally distributed. A robust procedure to select the smoothing parameter is also discussed.     

The t-information and its use in multivariate problems and time-series analysis

The second moment of recently introduced scalar inference function can be viewed as generalized Fisher information of the continuous probability distributions. In this paper we call it the t-information and give some possible applications of the new concept.     

中国人口老龄化的地区差异分析

本文基于1990—2013年省际面板数据,以老年人口比重作为老龄化指标,采用基尼系数分解方法和Kernel密度估计方法,实证分析了中国人口老龄化的地区差异及其动态演进过程。研究结果表明：中国人口老龄化在地区分布上呈现出明显的非均衡特征;基尼系数测算及其分解结果表明,人口老龄化的地区差异总体上呈现波动缩小趋势;2004年以前,地区间差距和超变密度交替成为总体地区差异的主要来源,2004年以后,超变密度成为总体差异的主要来源。 Kernel密度估计显示,老龄化程度不断加深,地区差异呈波动趋势。     

Testing independence based on Bernstein empirical copula and copula density

In this paper we provide three nonparametric tests of independence between continuous random variables based on the Bernstein copula distribution function and the Bernstein copula density function. The first test is constructed based on a Cramér-von Mises divergence-type functional based on the empirical Bernstein copula process. The two other tests are based on the Bernstein copula density and use Cramér-von Mises and Kullback–Leibler divergence-type functionals, respectively. Furthermore, we study the asymptotic null distribution of each of these test statistics. Finally, we consider a Monte Carlo experiment to investigate the performance of our tests. In particular we examine their size and power which we compare with those of the classical nonparametric tests that are based on the empirical distribution function.     

Uniform in bandwidth consistency for various kernel estimators involving functional data

The paper investigates various nonparametric models including regression, conditional distribution, conditional density and conditional hazard function, when the covariates are infinite dimensional. The main contribution is to prove uniform in bandwidth asymptotic results for kernel estimators of these functional operators. Then, the application issues, involving data-driven bandwidth selection, are discussed.     

非线性面板数据聚类方法研究

对于一类变量非线性相关的面板数据,现有的基于线性算法的面板数据聚类方法并不能准确地度量样本间的相似性,且聚类结果的可解释性低。综合考虑变量非线性相关问题及聚类结果可解释性问题,提出一种非线性面板数据的聚类方法,通过非线性核主成分算法实现对样本相似性的测度,并基于混合高斯模型进行样本概率聚类,实证表明该方法的有效性及其对聚类结果的可解释性有所提高。