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。     

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。     

Modeling probability density through ultraspherical polynomial transformations

We present a method for fitting parametric probability density models using an integrated square error criterion on a continuum of weighted Lebesgue spaces formed by ultraspherical polynomials. This approach is inherently suitable for creating mixture model representations of complex distributions and allows fully autonomous cluster analysis of high-dimensional datasets. The method is also suitable for extremely large sets, allowing post facto model selection and analysis even in the absence of the original data. Furthermore, the fitting procedure only requires the parametric model to be pointwise evaluable, making it trivial to fit user-defined models through a generic algorithm.     

Similar coefficient for cluster of probability density functions

In this article, we propose a new criterion to evaluate the similarity of probability density functions (pdfs). We call this the criterion on similar coefficient of cluster (SCC) and use it as a tool to deal with overlap coefficients of pdfs in normal standard on [0;1]. With the support of the self-update algorithm for determining the suitable number of clusters, SCC then becomes a criterion to establish the corresponding cluster for pdfs. Moreover, some results on determination of SCC in case of two and more than two pdfs as well as relations of different SCCs and other measures are presented. The numerical examples in both synthetic data and real data are given not only to illustrate the suitability of proposed theories and algorithms but also to demonstrate the applicability and innovation of the proposed algorithm.     

Asymptotic expansions of density of normalized extremes from logarithmic general error distribution

Logarithmic general error distribution is an extension of the log-normal distribution. In this paper, the asymptotic expansions of densities of normalized maximum from logarithmic general error distribution are derived under two different kinds of normalized constants. By applying the main results, the higher-order expansions of moments of maxima are established.     

Smooth estimation of a monotone density

We investigate the interplay of smoothness and monotonicity assumptions when estimating a density from a sample of observations. The nonparametric maximum likelihood estimator of a decreasing density on the positive half line attains a rate of convergence of [Formula: See Text] at a fixed point t if the density has a negative derivative at t. The same rate is obtained by a kernel estimator of bandwidth [Formula: See Text], but the limit distributions are different. If the density is both differentiable at t and known to be monotone, then a third estimator is obtained by isotonization of a kernel estimator. We show that this again attains the rate of convergence [Formula: See Text], and compare the limit distributions of the three types of estimators. It is shown that both isotonization and smoothing lead to a more concentrated limit distribution and we study the dependence on the proportionality constant in the bandwidth. We also show that isotonization does not change the limit behaviour of a kernel estimator with a bandwidth larger than [Formula: See Text], in the case that the density is known to have more than one derivative.     

Adaptive density estimation: A curse of support?

This paper deals with the classical problem of density estimation on the real line. Most of the existing papers devoted to minimax properties assume that the support of the underlying density is bounded and known. But this assumption may be very difficult to handle in practice. In this work, we show that, exactly as a curse of dimensionality exists when the data lie in R^d, there exists a curse of support as well when the support of the density is infinite. As for the dimensionality problem where the rates of convergence deteriorate when the dimension grows, the minimax rates of convergence may deteriorate as well when the support becomes infinite. This problem is not purely theoretical since the simulations show that the support-dependent methods are really affected in practice by the size of the density support, or by the weight of the density tail. We propose a method based on a biorthogonal wavelet thresholding rule that is adaptive with respect to the nature of the support and the regularity of the signal, but that is also robust in practice to this curse of support. The threshold, that is proposed here, is very accurately calibrated so that the gap between optimal theoretical and practical tuning parameters is almost filled.     

Nonparametric density deconvolution by weighted kernel estimators

Nonparametric density estimation in the presence of measurement error is considered. The usual kernel deconvolution estimator seeks to account for the contamination in the data by employing a modified kernel. In this paper a new approach based on a weighted kernel density estimator is proposed. Theoretical motivation is provided by the existence of a weight vector that perfectly counteracts the bias in density estimation without generating an excessive increase in variance. In practice a data driven method of weight selection is required. Our strategy is to minimize the discrepancy between a standard kernel estimate from the contaminated data on the one hand, and the convolution of the weighted deconvolution estimate with the measurement error density on the other hand. We consider a direct implementation of this approach, in which the weights are optimized subject to sum and non-negativity constraints, and a regularized version in which the objective function includes a ridge-type penalty. Numerical tests suggest that the weighted kernel estimation can lead to tangible improvements in performance over the usual kernel deconvolution estimator. Furthermore, weighted kernel estimates are free from the problem of negative estimation in the tails that can occur when using modified kernels. The weighted kernel approach generalizes to the case of multivariate deconvolution density estimation in a very straightforward manner.     

Differential equation models for statistical functions

Differential equations have been used in statistics to define functions such as probability densities. But the idea of using differential equation formulations of stochastic models has a much wider scope. The author gives several examples, including simultaneous estimation of a regression model and residual density, monotone smoothing, specification of a link function, differential equation models of data, and smoothing over complicated multidimensional domains. This paper aims to stimulate interest in this approach to functional estimation problems, rather than provide carefully worked out methods.     

高温条件下理想气体的相对论量子态

本文给出理想气体高温条件下的相对论量子统计态方程、能量和特征温度，并与非相对论情况做了类比和简析。