MPPS抽样下Hansen-Hurwitz估计量的扩展

一、问题的提出一般来说 ,在抽样调查中 ,我们会尽量利用已知的辅助信息来提高估计的精度。下面考虑一个包含N个单元的有限总体 ,设y表示我们要研究的变量 ,x是一个已知而且是正的辅助变量 ,且它与变量y高度相关 ,yi 和xi 分别表示第i个单元的研究变量值和辅助变量值 (其中i =1,… ,N)。这样在抽样设计阶段 ,我们就可以充分利用辅助变量x。比如说 ,在放回抽样中 ,我们以pi 作为第i个总体单元被抽中的概率 (其中i =1,… ,N) ,而pi 的大小取决于已知的辅助变量值xi,即满足pi >0 ,∑Ni =1pi =1。在这样一种放回的不等概率抽样中 ,如果让每个…     

曲线拟合分类与求解

一、问题的提出在科学实验或统计研究中,人们常常需要从一组测定的数据去求得自变量x和因变量y的一个解析表达式y=f(x)。如果不要求曲线通过所有的数据点,而只是要求它反映对象整体的变化趋势,则可得到简单实用的近似函数表达式y=f(x)。这是由给定的N个点(xi,yi)(i=1,2,     

习题解答(第六章)

习题6-11.1°已知f(x,y)=x-y/x~2 y~2;f(2,1)=(2-1)/2~2 1~2=1/5;f(a、b)=(a-b)/a~2 b~22°已知f(x,y)=x-4y/4x-y;f(1,1/2)=1-4×1/2/4×1-1/2=-2/7;f(a,b)=a-4b/4a-b     

通过对辅助变量的线性转化来改进比率估计

一、问题的提出在抽样调查中,比率估计方法是一种普遍应用的估计方法。在实践中,我们往往根据总体及辅助信息的特点建立不同的比率模型,然后再从该模型中得出不同的比率估计量。下面我们考虑一种最常用的比率模型ξ:假设x1,…,xN是已知的辅助变量值(都大于零),y1,…yN为未知的研究变量值,且总体单元总数为N。对于任意给定的xk,有以下关系yk=βxk εk同时对于所有的k∈K,满足Eξ(yk)=βxkVξ(yk)=σ2xk其中β和σ2为未知参数。在抽样总体满足该比率模型的前提下,进行简单随机抽样,可以得到一个非常经典的关于总体总值ty的比率估计量t^yra=…     

投资项目风险评价中的蒙特卡洛技术

一、蒙特卡洛模拟的基本原理设Y=f(x1,x2,Λ,xn),式中,x1,x2,Λ,xn是n个相互独立的随机变量,代表投资项目方案中的各参数,变量各服从一定的概率分布;Y是n个变量x的函数,例如投资项目的经济效益;f代表Y与x的函数关系,例如投资经济效益与其参数的关系。对各变量进行一次抽样,便可     

谈用“随机原则”确定直线等距抽样样本存在的问题

直线等距抽样就是先将总体各个单位按照一定的顺序排队,并根据总体单位数(N)和样本单位数(n)计算出抽选样本的间隔(k=N/n)。然后按照一定的间隔抽选样本单位。这实际上是将总体分为单位数相同的m段,每段中有k个单位。传统的样本抽选方法是先在1—k中产生一个随机数(i),则第一段中第i个位置上的单位就是被抽中的第一个样本单     

对统计预测中估计标准差的自由度的看法

在统计预测中,不论是时间序列预测还是因果关系预测,都是假设预测目标与影响其变化的因素之间存在着相关关系（线性或非线性）。因此,建立预测模型y=f（x1,对于一组样本观察值,满足,,且各;相互独立。为了研究y与x1,x2,...,xm之间的关系,我们建立回归模型,用所建立的预测模型进行预测和控制。对于给定的,则预测目标的点预测值为;当样本容量N较大时,预测目标的概率为95．45％的预测区间约为,其中为估计标准差（N为样本容量,m为模型中被估计参数的个数）。不论是线性模型还是非线性模型结论都是如此。由暴奉贤、韩兆洲、郭…     

多主题抽样中两种估计方法的比较

一、简单估计 所谓简单估计就是以样本单位标志值的平均值来直接作为总体标志值的估计值。由于简单估计的方法比较简单,因而在我国目前抽样调查的实践中应用比较普遍。 在多主题系统抽样中,假设有m个主题X_1、X_2、X_3…X_m,多主题总体单位数为N,从这N个总体单位中,按系统抽样的方式抽取n个样本单位,则多主题总体标志值X的简单估计量X为:     

这样计算简单相关系数合适吗?

简单相关系数根据资料形式的不同,大致可采用以下两种公式计算。在变量x,y都未分组的条件下,相关系数r_i≠sum (x－(?))(y-(?))/Nσ_xσ_y=(σ_(xy)~2)/(σ_xσ_y)(1)式中:x,y分别为两个变量的平均数;σ_X、σ_y分别为两个变量的标准差;σ_(xy)~2为两个变量的协方差;N为x,y的变量值个数。在x,y都已分组的条件下,相关系数     

习题解答(第五章)

l、2题略3.1°f(x)=3x~2, F(x)=x~3＋c2°f(x)=1/x, F(x)=ln|x|＋c3°f(x)=e~x, F(x)=e~x＋c4°f(x)=1/x~2, F(x)=-1/x＋c4.已知y=∫xdx=1/2 x~2＋c,将点(2,3)的坐标代入此式有:3=1/2×2~2＋c,得c=1,所以通过     

广义矩估计的延伸——广义经验似然估计

从广义矩估计(GMM)到广义经验似然估计(GEL)的发展,是由于GMM估计量小样本性质的不足,促使人们寻求方法的改进和拓展。通过必要的证明和推导,详细解析GEL类估计量(包括EL,ET,CUE)的逻辑关系和数理结构,认识GEL的内在本质,并运用随机模拟方法证实了在小样本场合GEL类估计量比GMM估计量具有更小的估计偏差和均方误差,即GEL类估计改进了GMM估计的小样本性质。     

Efficient Likelihood Estimators

If X ₁, …, X _n are identically and independently distributed, then as n ? ∞, there exists under suitable regularity conditions a sequence of solutions of the likelihood equation that is consistent and asymptotically efficient. However, this consistent solution is not necessarily the maximum likelihood estimate. Likelihood estimation should therefore emphasize the determination of a consistent sequence of solutions of the likelihood equations rather than maximizing the likelihood. The issues are illustrated on some examples.     

Asymptotic Properties and Variance Estimators of the M-quantile Regression Coefficients Estimators

M-quantile regression is defined as a “quantile-like” generalization of robust regression based on influence functions. This article outlines asymptotic properties for the M-quantile regression coefficients estimators in the case of i.i.d. data with stochastic regressors, paying attention to adjustments due to the first-step scale estimation. A variance estimator of the M-quantile regression coefficients based on the sandwich approach is proposed. Empirical results show that this estimator appears to perform well under different simulated scenarios. The sandwich estimator is applied in the small area estimation context for the estimation of the mean squared error of an estimator for the small area means. The results obtained improve previous findings, especially in the case of heteroskedastic data.     

Expressing Estimators of Expected Quality Adjusted Survival as Functions of Nelson-Aalen Estimators

Quality adjusted survival has been increasingly advocated in clinical trials to be assessed as a synthesis of survival and quality of life. We investigate nonparametric estimation of its expectation for a general multistate process with incomplete follow-up data. Upon establishing a representation of expected quality adjusted survival through marginal distributions of a set of defined events, we propose two estimators for expected quality adjusted survival. Expressed as functions of Nelson-Aalen estimators, the two estimators are strongly consistent and asymptotically normal. We derive their asymptotic variances and propose sample-based variance estimates, along with evaluation of asymptotic relative efficiency. Monte Carlo studies show that these estimation procedures perform well for practical sample sizes. We illustrate the methods using data from a national, multicenter AIDS clinical trial.     

Adaptive Warped Kernel Estimators

In this work, we develop a method of adaptive non‐parametric estimation, based on ‘warped’ kernels. The aim is to estimate a real‐valued function s from a sample of random couples (X,Y). We deal with transformed data (Φ(X),Y), with Φ a one‐to‐one function, to build a collection of kernel estimators. The data‐driven bandwidth selection is performed with a method inspired by Goldenshluger and Lepski (Ann. Statist., 39, 2011, 1608). The method permits to handle various problems such as additive and multiplicative regression, conditional density estimation, hazard rate estimation based on randomly right‐censored data, and cumulative distribution function estimation from current‐status data. The interest is threefold. First, the squared‐bias/variance trade‐off is automatically realized. Next, non‐asymptotic risk bounds are derived. Lastly, the estimator is easily computed, thanks to its simple expression: a short simulation study is presented.     

Consistency of Procrustes Estimators

Lele has shown that the Procrustes estimator of form is inconsistent and raised the question about the consistency of the Procrustes estimator of shape. In this paper the consistency of estimators of form and shape is studied under various assumptions. In particular, it is shown that the Procrustes estimator of shape is consistent under the assumption of an isotropic error distribution and that consistency breaks down if the assumption of isotropy is relaxed. The relevance of these results for practical shape analysis is discussed. As a by-product, some new results are derived for the offset uniform distribution from directional data.     

Estimators of Influence Function

ABSTRACT

This article discusses estimators of influence function with some new counter-examples and tries to uphold their usefulness mathematically as well as through simulation. It is suggested that some estimators of influence function of uniformly Fréchet differentiable functional has more desirable properties.     

On Minimum Variance Unbiased Estimators

The problem of finding minimum variance unbiased estimators of various parameters for parametric distributions is an important one in statistics. This article gives analytical formulas for the minimum variance unbiased estimators of parametric functions, which are usually used in a classroom, for two types of densities. The first type is the one-parameter regular exponential family, and the second is a two-parameter family of a continuous random variable whose range depends on the unknown parameters.     

Zero-Bias Locally Adaptive Density Estimators

Strategies for improving fixed non-negative kernel estimators have focused on reducing the bias, either by employing higher-order kernels or by adjusting the bandwidth locally. Intuitively, bandwidths in the tails should be relatively larger in order to reduce wiggles since there is less data available in the tails. We show that in regions where the density function is convex, it is theoretically possible to find local bandwidths such that the pointwise bias is exactly zero. The corresponding pointwise mean squared error converges at the parametric rate of O ( n ⁻¹ ) rather than the slower O ( n ^−4/5). These so-called zero-bias bandwidths are constant and are usually orders of magnitude larger than the optimal locally adaptive bandwidths predicted by asymptotic mean squared error analysis. We describe data-based algorithms for estimating zero-bias bandwidths over intervals where the density is convex. We find that our particular density estimator attains the usual O ( n ^−4/5) rate. However, we demonstrate that the algorithms can provide significant improvement in mean squared error, often clearly visually superior curves, and a new operating point in the usual bias-variance tradeoff.