一种收入分布函数序列的拟合方法及扩展应用

针对收入分布函数形式选择问题,提出具有＂自适应＂能力的收入分布序列拟合思路,给出基于B-样条的收入分布函数形式,并对收入分布参数进行最小二乘估计。拟合了中国历年城镇居民收入分布序列;导出中国1996-2009年洛伦兹曲线和基尼系数;从函数角度刻画了城镇居民收入水平不断提高的同时,收入差距扩大的动态趋势;验证了城镇居民收入差距的变动轨迹体现着＂阶梯形＂扩大的特征。     

中国城乡收入分布动态演进及经验检验

 本文利用“中国营养和健康调查”家庭平均收入的模块数据,采用核密度估计、累积分布函数、洛伦茨曲线等方法刻画我国城乡居民收入分布的动态演进,结果表明,大多数家庭都分享了我国经济快速增长带来的成果,其中高收入阶层比低收入阶层更多地分享了经济成果,城市居民比农村居民更多地分享了经济成果。进一步地,非参数假设检验和相对偏差估计结果表明,N. C. Kakwani的三参数函数和简易公式的基尼系数估计精度较高,广义Logistic分布函数是估计城乡居民收入分布的可行选择。     

收入分布函数在收入不平等研究领域的应用

收入分布函数的研究对于收入不平等的探讨意义重大,但是国内该领域的研究尚待进一步的拓展。笔者在文献回顾的基础上梳理了国外的相关研究,把常用的分布函数分成两参数分布和多参数分布函数两类,介绍了各类函数拟合居民实际收入分布的效果及其与基尼系数的关系;同时,本文剖析了各类函数之间的内在联系;进一步,基于分布函数的角度,笔者研究了收入流动性与收入不平等之间的联系,分析了平均数和中位数比值与基尼系数之间的数学关系以及如何利用该比值来估计分布函数的参数。利用这些研究结果,文章探讨了2010年我国城镇居民的收入结构和贫困问题并提出了一些前瞻性的研究建议。     

指数族分布中参数的经验贝叶斯估计

指数族分布是一类应用广泛的分布类,包括了泊松分布、Gamma分布、Beta分布、二项分布等常见分布.在非寿险中,索赔额或索赔次数过程常常被假定服从指数族分布,由于风险的非齐次性,指数族分布中的参数θ也为随机变量,假定服从指数族共轭先验分布.此时风险参数的估计落入了Bayes框架,风险参数θ的Bayes估计被表达“信度”形式.然而,在实际运用中,由于先验分布与样本分布中仍然含有结构参数,根据样本的边际分布的似然函数估计结构参数,从而获得风险参数的经验Bayes估计,最后证明了该经验Bayes估计是渐近最优的.     

三参数Burr分布经验贝叶斯估计的渐近性

文章在平方损失下研究三参数BurrI分布族形状参数的经验贝叶斯(EB)估计的渐近性。在先验分布形式未知的情况下,采用非参数估计方法导出了BurrI分布族形状参数的贝叶斯(Bayes)估计,利用历史样本采用密度函数核估计方法,构造了边缘密度函数及其导函数的估计,将它们代入Bayes估计式中,得到了形状参数的EB估计。在一定的条件下,证明所得到的EB估计具有渐近性,其收敛速度为n-γ(s-1)(δ-2)/δ(2s+1)。文章还举例说明满足定理条件的参数的先验分布是存在的。     

最低生活保障线的收入分布函数测算方法

测算城镇居民最低生活保障线的方法有很多种,但很多方法以营养标准为基础,可操作性不强。文章提出采用收入分布函数对贫困线进行测算的方法,并通过对北京市相关数据的实证分析,给出科学的测算结果。表明该方法具有科学性和较强的实用性。     

加权平方损失函数和MUNEX损失函数下一类分布族参数的Minimax估计

文章考虑一类分布族:F(x;θ)=1-[g(x)]θ(A≤x≤B,θ>0),其中g(x)是关于x单调递减的可微函数,且g(A)=1,g(B)=0,在加权平方损失函数和MLINEX损失函数下,得到了参数的Bayes估计和Minimax估计.     

逐步增加的Ⅱ型截尾下Lomax分布形状参数的估计

文章在逐步增加的Ⅱ型截尾下,给出了Lomax分布形状参数θ的极大似然估计;由“平均剩余寿命”的概念得到了形状参数的逆矩估计,在平方损失函数和对称熵损失函数下,针对不同的先验分布给出了参数θ的Bayes估计;最后通过随机模拟对几个估计进行了比较,说明了在相同的损失函数下,取共轭先验分布较无信息先验分布的精度要高.     

组合分布在我国居民收入分布拟合中的应用研究

本文使用CHNS家庭微观调查数据研究发现,我国城乡居民家庭人均收入对数呈现明显左偏特征.为了适应这种需要,根据组合分布函数的构造思想,首次提出使用反向帕累托-对数正态组合分布对居民收入数据进行建模,并使用极大似然估计方法对参数进行估计.然后,使用变量替换方法导出我国城乡居民收入原始数据的确切分布,并在此基础上,测算出我国城乡居民收入的基尼系数.实证研究发现,农村居民家庭人均收入基尼系数高于城镇居民,就基尼系数发展态势而言,我国城乡居民家庭收入基尼系数在2007-2009年间发生了转变,由之前的持续增加转为下降.     

几何分布可靠度的Bayse估计

本文在几何分布可靠度的先验分布为幂分布时,给出了其在熵损失函数下的E-Bayes估计公式和多层Bayes估计公式。通过实例验证,这两种估计公式都是稳健的,并进一步表明了在熵损失函数下计算出的几何分布可靠度的E-Bayes估计值比多层Bayes估计值的精度更高,稳健性更好,计算简单,便于应用。     

Smooth tests of goodness of fit for regular distributions

Smooth tests of goodness of fit based on orthonormal functions for location-scale families were introducedin Rayner and Best (1986).This paper extends this class of tests from location -scale families to ‘regular’ families. The extension preserves the desirable properties of the class, such as weak optimality, accessible components and convenient distribution theory     

Partially-adaptive robust estimators of location via exponential embedding

A general method is presented for constructing a location estimator which is asymptotically efficient at any two different location-scale families of symmetric distributions as well as at an appropriately defined class of distributions lying in between. The method works by embedding the two families in a comprehensive parametric model and identifying the estimator with the MLE. The case when the families are Normal and Double exponential is examined in detail.     

MLE of parameters of location-scale distribution for complete and partially grouped data

This article studies the MLEs of parameters of location-scale distribution functions. It gives the necessary and sufficient conditions under which the MLEs of the location and scale parameters uniquely exist with completely grouped data. The results for the completely grouped data further imply that the Pearson–Fisher test is applicable to location-scale families. It also gives sufficient conditions under which the MLEs of the two parameters uniquely exist with partially grouped data. Here, the partially grouped data include complete data, Type-I censored data and others as special cases.     

Random number generation and estimation with the bimodal asymmetric power-normal distribution

Recently, Bolfarine et al. [Bimodal symmetric-asymmetric power-normal families. Commun Statist Theory Methods. Forthcoming. doi:10.1080/03610926.2013.765475] introduced a bimodal asymmetric model having the normal and skew normal as special cases. Here, we prove a stochastic representation for their bimodal asymmetric model and use it to generate random numbers from that model. It is shown how the resulting algorithm can be seen as an improvement over the rejection method. We also discuss practical and numerical aspects regarding the estimation of the model parameters by maximum likelihood under simple random sampling. We show that a unique stationary point of the likelihood equations exists except when all observations have the same sign. However, the location-scale extension of the model usually presents two or more roots and this fact is illustrated here. The standard maximization routines available in the R system (Broyden–Fletcher–Goldfarb–Shanno (BFGS), Trust, Nelder–Mead) were considered in our implementations but exhibited similar performance. We show the usefulness of inspecting profile loglikelihoods as a method to obtain starting values for maximization and illustrate data analysis with the location-scale model in the presence of multiple roots. A simple Bayesian model is discussed in the context of a data set which presents a flat likelihood in the direction of the skewness parameter.     

Estimation for Location-Scale Models with Censored Data

This article considers a class of estimators for the location and scale parameters in the location-scale model based on ‘synthetic data’ when the observations are randomly censored on the right. The asymptotic normality of the estimators is established using counting process and martingale techniques when the censoring distribution is known and unknown, respectively. In the case when the censoring distribution is known, we show that the asymptotic variances of this class of estimators depend on the data transformation and have a lower bound which is not achievable by this class of estimators. However, in the case that the censoring distribution is unknown and estimated by the Kaplan–Meier estimator, this class of estimators has the same asymptotic variance and attains the lower bound for variance for the case of known censoring distribution. This is different from censored regression analysis, where asymptotic variances depend on the data transformation. Our method has three valuable advantages over the method of maximum likelihood estimation. First, our estimators are available in a closed form and do not require an iterative algorithm. Second, simulation studies show that our estimators being moment-based are comparable to maximum likelihood estimators and outperform them when sample size is small and censoring rate is high. Third, our estimators are more robust to model misspecification than maximum likelihood estimators. Therefore, our method can serve as a competitive alternative to the method of maximum likelihood in estimation for location-scale models with censored data. A numerical example is presented to illustrate the proposed method.     

On prediction from the location-scale model with equi correlated response

The location-scale model with equi-correlated responses is discussed. The structure of the location-scale model is utilised to genera-te the prediction distribution of a future response and that of a set of future responses. The method avoids the integration procedures usually involved in derivation of prediction distributions and yields results same as those obtained by the Bayes method with the vague prior distribution* Finally the re-suits have been specialised to cover the case of the normal intra-class model.     

Estimating quantiles using optimally selected order statistics

This expository paper deals with the linear estimation of quantiles of location-scale families of distributions using a few selected order statistics.The general theory for the problem i s reviewed for the exact as well as the asymptotic cases.     

Fisher Information in Record Values and Their Concomitants: A Comparison of Two Sampling Schemes

Two sampling designs via inverse sampling for generating record data and their concomitants are considered: single sample and multisample. The purpose here is to compare the Fisher information in these two sampling schemes. It is shown that the comparison criterion depends on the underlying distribution. Several general results are established for some parametric families and their well known subclasses such as location-scale and shape families, exponential family and proportional (reversed) hazard model. Farlie-Gumbel-Morgenstern (FGM) family, bivariate normal distribution, and some other common bivariate distributions are considered as examples for illustrations and are classified according to this criterion.     

RANKED SET SAMPLING FROM LOCATION-SCALE FAMILIES OF SYMMETRIC DISTRIBUTIONS

Statistical inference based on ranked set sampling has primarily been motivated by nonparametric problems. However, the sampling procedure can provide an improved estimator of the population mean when the population is partially known. In this article, we consider estimation of the population mean and variance for the location-scale families of distributions. We derive and compare different unbiased estimators of these parameters based on rindependent replications of a ranked set sample of size n.Large sample properties, along with asymptotic relative efficiencies, help identify which estimators are best suited for different location-scale distributions.     

Robust generalized confidence intervals

Most interval estimates are derived from computable conditional distributions conditional on the data. In this article, we call the random variables having such conditional distributions confidence distribution variables and define their finite-sample breakdown values. Based on this, the definition of breakdown value of confidence intervals is introduced, which covers the breakdowns in both the coverage probability and interval length. High-breakdown confidence intervals are constructed by the structural method in location-scale families. Simulation results are presented to compare the traditional confidence intervals and their robust analogues.