符号检验的最优排序集抽样(英文)

提出检验总体分位数的基于排序集抽样的符号检验,分析了不同挑选抽样相对于均衡抽样的Pitman渐近效率.针对不同分位数,具体给出使符号统计量的效率达到最大的抽样设计,并且证明了最优抽样不依赖于总体分布.     

基于样本次序统计量的总体分位数的非参数统计推断

随机总体分位数的统计推断理论与方法一直是统计学研究的重要课题.其主要原因是分位数的应用涉及众多领域,且在各领域的研究中起到举足轻重的作用.本文系统地论述了基于样本次序统计量的总体分位数的非参数统计推断的理论和方法;给出了基于样本次序统计量的总体分位数的估计方法,总体两个分位数之差的置信区间,总体容许区间的求解方法及符号检验.希望有助于读者的科研与应用.     

φ-混合样本下有限个分位数估计的联合渐近分布

本文研究了φ-混合样本下总体的有限个分位数核估计的渐近性质.利用分块技术证明了φ-混合样本下总体的有限个分位数核估计的联合渐近分布为多元正态分布,推广了文献[16]的相关结果.     

基于外推中间次序分位数的极端条件分位数估计

近年来,条件分位数估计被广泛应用于金融、生物和医学等众多领域.在研究协变量对响应变量在不同分位数水平的影响时,分位数回归方法是一种贴切且有效的估计方法.然而,由于尾部数据的稀疏性,用分位数回归来估计极端条件分位数通常会产生较大的估计误差.文章将极值理论与分位数回归结合起来,利用中间条件分位数外推法,研究线性分位数回归模...     

无交叉多指标分位数回归模型中的估计与推断

在过去的30年中分位数回归模型的研究已十分深入.然而在实际的应用场景中,由传统估计方法所得到的分位数回归估计量,经常会在不同分位数水平上出现互相交叉的现象,这给分位数回归模型的实际应用造成了解释和预测上的困难.为解决这个问题,本文提出一种带单调约束的半参数多指标分位数回归模型的研究框架.首先将半参数多指标分位数回归模型...     

核分位数估计及其应用

分位数估计在不同领域有大量的运用,例如水利研究中百年一遇的洪水、金融分析中的VaR.不过在实际应用中大多使用的是基于正态假设下的分位数估计,使得该方法在应用时存在模型设定错误的风险.为此介绍了稳健统计中非参数分位数估计方法,并介绍了核密度估计的步骤;然后以此为基础给出了核分位数估计,同时还通过重复抽样的方法比较了三种分位数估计效果和积累了使用经验;最后给出了一个金融数据的实例,并对核分位数估计结果和常用的正态假设下的分位数结果进行了比较,结果显示核分位数估计结果更加稳健.文中最后给出使用该方法的一些建议.     

基于Monte Carlo模拟比较K近邻和局部线性分位数回归

局部线性分位数回归是目前比较流行的非参数分位数回归,其潜在假定待估函数线性光滑.K近邻分位数回归也是非参数分位数回归的重要组成部分,其具有不需待估函数光滑和不同分位点的回归曲线不相交等优点.通过Monte Carlo模拟,比较了两者的估计,得到当待估函数的跳跃点或突变点比较多时,K近邻分位数回归的拟合效果优于局部线性回归.其中模拟的函数是Blocks、Bumps和HeaviSine的函数,它们分别代表跳跃性、波动性、斜率突变性的函数.     

基于二阶指数混料模型的A-最优设计

使用最优设计理论研究混料试验的过程中,需考虑混料模型对应的函数向量。当函数向量为非线性函数时,虽可使用Taylor级数进行近似,但级数阶的选取必然使得误差的存在,给试验带来偏差。本文旨在使用最优设计理论,研究q分量二阶指数混料模型的A-最优设计问题,并得到了该模型下的最优设计。且从设计效率的角度,研究了不同分量下的A-最优设计效率,为确定设计优劣提供了一个依据,并给出了进一步可以研究的问题。     

不完美排序集抽样的符号检验

实际应用排序集抽样时,主观排序总是会出现误差。本文考虑了不完美排序对最优抽样下分位数符号检验的影响,并且给出不同分位数的误差函数图像,以便具体应用时参考。     

基于分位数回归的国内黄金价格影响因素分析

利用分位数回归模型探讨了原油价格、道琼斯指数、美元汇率、上证指数和利率对国内黄金价格的影响.实证分析结果表明在不同分位数水平上各因素对黄金价格的影响不一样.分位数回归能够从历史数据中挖掘出更多的信息,更有利于投资者了解影响黄金价格波动的因素从而做出更好的决策.     

Quantile estimation under successive sampling

This paper deals with the estimation, under sampling in two successive occasions, of a finite population quantile. For this sampling design a class of estimators is proposed whose the ratio and difference estimators are particular cases. Asymptotic variance formulae are derived for the proposed estimators, and the optimal matching fraction is discussed. Comparisons are made with existing estimators in a simulation study using a natural population.     

Quantile Processes in the Presence of Auxiliary Information

We employ the empirical likelihood method to propose a modified quantile process under a nonparametric model in which we have some auxiliary information about the population distribution. Furthermore, we propose a modified bootstrap method for estimating the sampling distribution of the modified quantile process. To explore the asymptotic behavior of the modified quantile process and to justify the bootstrapping of this process, we establish the weak convergence of the modified quantile process to a Gaussian process and the almost-sure weak convergence of the modified bootstrapped quantile process to the same Gaussian process. These results are demonstrated to be applicable, in the presence of auxiliary information, to the construction of asymptotic bootstrap confidence bands for the quantile function. Moreover, we consider estimating the population semi-interquartile range on the basis of the modified quantile process. Results from a simulation study assessing the finite-sample performance of the proposed semi-interquartile range estimator are included.     

Simulation Estimation of Quantiles From a Distribution With Known Mean

It is common in practice to estimate the quantiles of a complicated distribution by using the order statistics of a simulated sample. If the distribution of interest has known population mean, then it is often possible to improve the mean square error of the standard quantile estimator substantially through the simple device of mean-correction: subtract off the sample mean and add on the known population mean. Asymptotic results for the meancorrected quantile estimator are derived and compared to the standard sample quantile. Simulation results for a variety of distributions and processes illustrate the asymptotic theory. Application to Markov chain Monte Carlo and to simulation-based uncertainty analysis is described.     

Minimax and maximin efficient designs for estimating the location-shift parameter of parallel models with dual responses

Minimax designs and maximin efficient designs for estimating the location-shift parameter of a parallel linear model with correlated dual responses over a symmetric compact design region are derived. A comparison of the behavior of efficiencies between the minimax and maximin efficient designs relative to locally optimal designs is also provided. Both minimax or maximin efficient designs have advantage in terms of estimating efficiencies in different situations.     

On representing block designs in terms of their automorphisms

Block designs are analyzed in terms of the structure imposed upon them by their automorphisms. An extension of the notion of a difference set is used to describe necessary and sufficient conditions for the existence of a given automorphism acting on the design. In addition, it is shown that the possible point and block orbit configurations relative to an automorphism acting on a design are rather limited. The development is carried out with a view toward finding unknown designs and studying the automorphism groups of known designs.     

Local Weighted Composite Quantile Estimating for Varying Coefficient Models

A generalization of classical linear models is varying coefficient models, which offer a flexible approach to modeling nonlinearity between covariates. A method of local weighted composite quantile regression is suggested to estimate the coefficient functions. The local Bahadur representation of the local estimator is derived and the asymptotic normality of the resulting estimator is established. Comparing to the local least squares estimator, the asymptotic relative efficiency is examined for the local weighted composite quantile estimator. Both theoretical analysis and numerical simulations reveal that the local weighted composite quantile estimator can obtain more efficient than the local least squares estimator for various non-normal errors. In the normal error case, the local weighted composite quantile estimator is almost as efficient as the local least squares estimator. Monte Carlo results are consistent with our theoretical findings. An empirical application demonstrates the potential of the proposed method.     

A Frisch-Newton Algorithm for Sparse Quantile Regression

Recent experience has shown that interior-point methods using a log barrier approach are far superior to classical simplex methods for computing solutions to large parametric quantile regression problems. In many large empirical applications, the design matrix has a very sparse structure. A typical example is the classical fixed-effect model for panel data where the parametric dimension of the model can be quite large, but the number of non-zero elements is quite small. Adopting recent developments in sparse linear algebra we introduce a modified version of the Prisch-Newton algorithm for quantile regression described in Portnoy and Koenker~([28]). The new algorithm substantially reduces the storage (memory) requirements and increases computational speed. The modified algorithm also facilitates the development of nonparametric quantile regression methods. The pseudo design matrices employed in nonparametric quantile regression smoothing are inherently sparse in both the fidelity and roughness penalty components. Exploiting the sparse structure of these problems opens up a whole range of new possibilities for multivariate smoothing on large data sets via ANOVA-type decomposition and partial linear models.     

Estimation of a smooth quantile function under the proportional hazards model

The problem of estimating a smooth quantile function, Q(·), at a fixed point p, 0 < p < 1, is treated under a nonparametric smoothness condition on Q. The asymptotic relative deficiency of the sample quantile based on the maximum likelihood estimate of the survival function under the proportional hazards model with respect to kernel type estimators of the quantile is evaluated. The comparison is based on the mean square errors of the estimators. It is shown that the relative deficiency tends to infinity as the sample size, n, tends to infinity.     

An improved class of estimators of a finite population quantile in sample surveys

This work proposes a general class of estimators for a finite population quantile using auxiliary information. This information is provided by the population means of auxiliary variables. The optimum estimator in this class is derived. This result is supported with a numerical example.