收入基尼系数的统计推断

 基尼系数估计量的统计推断是基尼系数研究的一个重点。本文我们使用Davidson（2009）提出的近似大样本渐进分布方法,对收入基尼系数估计量进行统计推断,包括计算估计量的标准差、构造置信区间和进行假设检验。通过模拟试验,我们验证了在小样本情形下,依据该方法所做的统计推断具有较高的可靠性。在此基础上,我们对我国城镇居民的真实收入基尼系数进行了统计推断。     

基于倾向值加权的网络调查总体参数Horvitz-Thompson估计

基于倾向值加权方法,构造了网络调查总体参数的Horvitz-Thompson无偏估计量,并对其方差进行了讨论,分析了利用插补缺失数据后的网络调查样本进行总体参数估计的方差来源,在网络调查总体参数估计方面提出了新的思路和方法。     

变环境情形下对数正态分布分组数据的参数估计

文章研究了变环境情形下对数正态分布分组数据可靠性估计的参数估计问题.给出了一种基于EM算法的变环境分组数据Log-normal分布参数估计方法,其估计量具有良好的收敛性,模拟结果表明了这种方法的实践可操作性.     

洛伦兹曲线的半参数估计

洛伦兹曲线与基尼系数是研究社会收入分配差异的重要工具.社会收入分配是一个复杂的过程,用尽可能精确的曲线给出洛伦兹曲线的估计进而给出基尼系数的估计,历来是统计学者和经济学者的工作目标.基于将参数方法与非参数方法相结合的思想给出洛伦兹曲线的半参数估计,进而导出基尼系数的估计,并据此进行了实证分析.     

中国耕地生产率发展水平的地区差距及其分布动态演进

文章采用了Dagum基尼系数与非参数估计方法实证研究中国1997-2013年耕地生产率发展水平的地区差距及其演变态势,实证结果表明:中国耕地生产率发展水平在地区分布上呈现明显的非均衡特征,并且地区差异呈增大趋势;利用基尼系数及其子群分解方法对中国耕地生产率发展水平进行了地区分解,结果显示地区间差距对地区差异贡献率呈现先减小、后增大的演变态势,地区内差距贡献率变化几乎不明显,而超变密度贡献率则呈先上升、后下降演变态势.核密度估计表明我国东中西三大地区耕地生产率发展水平呈现出不同的演变态势,总体呈扩大态势.     

第三类Tobit模型的半参数估计方法

本文给出第三类Tobit模型的一种新的半参数估计方法。在独立性假设下,利用主方程和选择方程中可观察受限因变量的条件生存函数所满足的关系式,构造第三类Tobit模型参数的一步联立估计量。在已知选择方程中参数一致性估计量的条件下,这种方法也可用于构造主方程模型参数 的两步估计量。本文证明了所提出的一步联立估计量和两步估计量的一致性和渐近正态性。实验模拟表明,我们提出的估计量在有限样本下具有良好表现,且一步联立估计量的有限样本表现优于或接近于Chen（1997）的估计量。     

上证综合指数极值分布参数估计的实证分析

文章选取了2013年4月1日至2014年4月21日的所有交易日的上证综指,采用区间选取方法选取每一小时中上证综指的最大值为广义极值分布的样本数据.分别采用线性矩法和最大间隔积估计法对样本数据进行拟合,计算出广义极值分布参数的估计量,通过Q-Q分位数图和K-S检验对参数估计量的拟合优度进行检验.并通过PPCC检验法和均方根误差RMSE对不同估计方法计算出的参数估计量进行比较,得出在此条件下较优的参数估计结果.     

PPS抽样下子总体参数的估计

本文讨论PPS抽样下子总体参数的估计,给出基于PPS总体抽样和基于总体抽样及子总体追加PPS抽样的子总体参数的估计量,并分析了估计量的性质.     

基于核实数据的正态总体估计问题探讨

文章研究基于替代与核实数据样本下正态总体的参数估计问题，利用核权函数法构造估计量。得到该估计量的强相合性及渐进正态性，从而给出参数的区间估计。最后用实例验证该估计量的合理性。     

均值估计时样本容量的确定

通过样本观察进行认识是统计方法的基本特征,在这一过程中,多大规模的样本才能符合研究的需要,始终是个回避不了的问题。本文从估计精度要求出发,对总体参数估计时样本容量的确定进行了讨论。     

Gini index estimation for lifetime data

Lifetime data is often right-censored. Recent literature on the Gini index estimation with censored data focuses on independent censoring. However, the censoring mechanism is likely to be dependent censoring in practice. This paper proposes two estimators of the Gini index under independent censoring and covariate-dependent censoring, respectively. The proposed estimators are consistent and asymptotically normal. We also evaluate the performance of our estimators in finite samples through Monte Carlo simulations. Finally, the proposed methods are applied to real data.     

Linearization of inequality indices in the design-based framework

Linearization methods are customarily adopted in sampling surveys to obtain approximated variance formulae for estimators of statistical functionals under the design-based approach. In the present paper, following the Deville [Variance estimation for complex statistics and estimators: linearization and residual techniques. Surv Methodol. 1999;25:193–203] approach stemming from the concept of design-based influence function, we provide a general result for linearizing a large family of population functionals which includes many of the inequality measures considered in social, economic and statistical studies, such as the Gini, Amato, Zenga, Atkinson and Generalized Entropy indices. The feasibility of our theoretical results is assessed by some simulation studies involving real and artificial data.     

Comparison of the several parameterized estimators for the positive extreme value index

In the paper we compare several parameterized estimators for the positive extreme value index, which is a very important parameter appearing in the estimation of the probability of rare events. Firstly, asymptotic comparison at optimal levels of the corresponding tail index estimators is performed. Secondly, the practical validation of asymptotic results for moderate finite samples is done by means of Monte-Carlo simulations. We demonstrate that theoretical domination of the positive extreme value index estimators, which are asymptotically normal with a null asymptotic bias, is not reflected in Monte-Carlo simulations. Moreover, the estimators of such type do not demonstrate stability in the sense of empirical mean-squared error.     

Bias reduction in the estimation of a shape second-order parameter of a heavy-tailed model

In extreme value theory, the shape second-order parameter is a quite relevant parameter related to the speed of convergence of maximum values, linearly normalized, towards its limit law. The adequate estimation of this parameter is vital for improving the estimation of the extreme value index, the primary parameter in statistics of extremes. In this article, we consider a recent class of semi-parametric estimators of the shape second-order parameter for heavy right-tailed models. These estimators, based on the largest order statistics, depend on a real tuning parameter, which makes them highly flexible and possibly unbiased for several underlying models. In this article, we are interested in the adaptive choice of such tuning parameter and the number of top order statistics used in the estimation procedure. The performance of the methodology for the adaptive choice of parameters is evaluated through a Monte Carlo simulation study.     

Stabilizing the Performance of Kurtosis Estimator of Multivariate Data

The estimation of the kurtosis parameter of the underlying distribution plays a central role in many statistical applications. The central theme of the article is to improve the estimation of the kurtosis parameter using a priori information. More specifically, we consider the problem of estimating kurtosis parameter of a multivariate population when some prior information regarding the the parameter is available. The rationale is that the sample estimator of the kurtosis parameter has a large estimation error. In this situation we consider shrinkage and pretest estimation methodologies and reappraise their statistical properties. The estimation based on these strategies yield relatively smaller estimation error in comparison with the sample estimator in the candidate subspace. A large sample theory of the suggested estimators are developed and compared. The results demonstrate that suggested estimators outperform the estimator based on the sample data only in the candidate subspace. In an effort to appreciate the relative behavior of the estimators in a finite sample scenario, a Monte-carlo simulation study is planned and performed. The result of simulation study strongly corroborates the asymptotic result. To illustrate the application of the estimators, some example are showcased based on recently published data.     

基尼加权回归分析：概念、方法及应用

普通最小二乘法是进行回归分析最常用的基本方法,但该方法要求满足若干经典假设,对于小样本或在与收入相关回归分析的参数估计中易受奇异值、高收入群体的影响。本文试图利用基尼加权回归弥补以上不足。基尼加权回归可分为参数方法与非参数方法两类,参数方法基于样本残差的基尼平均差最小原则对参数进行估计;非参数方法则是直接由两点间的斜率加权得到。基尼加权回归分析可以进行参数假设检验并定义拟合优度,其中的假设检验在实际应用中采用Jackknife重抽样方法估计方差。文中提出的样本拓展基尼平均差算法,弥补了现有算法对样本数据只能提供近似计算的不足,极大简化相应的计算公式。利用我国2015年省域截面数据、1994至2015年总量时间序列数据分别讨论入境旅游收入对收入基尼系数的影响,发现使用基尼加权回归的结果不仅符合理论预期,而且可以通过不平等厌恶参数的变化反映入境旅游收入对不同群体收入公平性的影响。     

New calibration estimator in stratified sampling

This paper develops the theory of calibration estimation and proposes calibration approach alternative to existing calibration estimators for estimating population mean of the study variable using auxiliary variable in stratified sampling. The theory of new calibration estimation is given and optimum weights are derived. A simulation study is carried out to performance of the proposed calibration estimator with other existing calibration estimators. The results reveal that the proposed calibration estimators are more efficient than Tracy et al., Singh et al., Singh calibration estimators of the population mean.     

中国城镇化的地域非均衡及其动态演进——来自基尼系数及核密度估计的经验证据

基于中国1995-2013年省域数据,采用基尼系数及其分解、核密度估计方法,从人口和土地城镇化入手,系统分析了中国城镇化的地域非均衡及其动态演化规律。结果发现,1.中国人口和土地城镇化分布均呈现出由东往西逐渐降低的规律,城镇化非均衡主要体现在土地城镇化,而人口城镇化则未出现明显分异。2.全国尺度人口城镇化基尼系数随时间不断下降,城镇化非均衡逐渐减小;土地城镇化基尼系数则呈倒"U"型,城镇化非均衡先增后减。3.东中西三大区域内人口城镇化基尼系数均呈直线下降,区域间非均衡东部最大,西部次之,中部最小;土地城镇化非均衡则是东部大于中部和西部,但近年来西部已超过东部。4.人口城镇化非均衡在1995-2001年间主要来自地区间重叠,而后2002-2013年主要由地区间差异驱动;土地城镇化非均衡则主要来源于地区间差异。5.核密度估计显示人口城镇化增速较快,波动较小,而土地城镇化则极化趋势明显,波动较大。新型城镇化的协调推进宜从人口和土地城镇化两方面着手,特别要注意土地城镇化的失衡发展问题。     

Statistical inference for some Volterra population processes in a random environment

We study the problem of maximum-likelihood estimation in some random-environment population models, defined through nonlinear ltǒ stochastic differential equations. It is shown that a criticality parameter can be estimated consistently. The asymptotic behavior of the estimators is analyzed, and a goodness-of-fit test is proposed.