用辅助变量分层和将其用于比估计抽样效果的比较

本文给出用辅助变量构造比估计量的抽样效果优于用它分层的条件。     

删失回归模型中的变量选择

在一个删失回归模型("Tobit"模型)中,我们常常要研究如何选择重要的预报变量.本文提出了基于信息理论准则的两种变量选择程序,并建立了它们的相合性.     

Bayesian Variable Selection for Logistic Models Using Auxiliary Mixture Sampling

This article presents a Markov chain Monte Carlo algorithm for both variable and covariance selection in the context of logistic mixed effects models. This algorithm allows us to sample solely from standard densities with no additional tuning. We apply a stochastic search variable approach to select explanatory variables as well as to determine the structure of the random effects covariance matrix.

Prior determination of explanatory variables and random effects is not a prerequisite because the definite structure is chosen in a data-driven manner in the course of the modeling procedure. To illustrate the method, we give two bank data examples.     

Confidence intervals for quantile estimation using Jackknife techniques

We consider the inference on quantiles, Q _y (β), with jackknife techniques, in finite populations of a variable, Y, using the quantile information on an auxiliary variable, X. Jackknife techniques are applied to estimate quantiles and the behaviour of these estimators is analyzed. Their properties are studied for simple random sampling. We also examine the confidence intervals obtained with jackknife variances.     

Bootstrap for the conditional distribution function with truncated and censored data

We propose a resampling method for left truncated and right censored data with covariables to obtain a bootstrap version of the conditional distribution function estimator. We derive an almost sure representation for this bootstrapped estimator and, as a consequence, the consistency of the bootstrap is obtained. This bootstrap approximation represents an alternative to the normal asymptotic distribution and avoids the estimation of the complicated mean and variance parameters of the latter.     

Ratio methods to the mean estimation with known quantiles

We address the problem of estimating the finite population mean in survey sampling, by exploiting any available auxiliary information in order to increase the precision of classical estimators. The idea is to use any population quantiles of the available auxiliary variables which are known in many real situation from census, administrative files, etc. This is achieved using these known quantities in the construction of the estimators, by modifying the usual ratio estimation methods and afterwards defining a general class of exponentiation ratio estimators. The advantages of the proposed estimators are demonstrated using theoretical asymptotic tools and through a simulation study.     

Bootstrap tests for unit roots in seasonal autoregressive models

This paper proposes bootstrap tests for the presence of unit roots in a seasonal autoregressive model. The asymptotic validity of the proposed bootstrap scheme is established, and Monte Carlo experiments are used to investigate the small-sample performance of the tests.     

响应变量随机缺失下变系数模型的变量选择

In this paper,we present a variable selection procedure by combining basis function approximations with penalized estimating equations for varying-coefficient models with missing response at random.With appropriate selection of the tuning parameters,we establish the consistency of the variable selection procedure and the optimal convergence rate of the regularized estimators.A simulation study is undertaken to assess the finite sample performance of the proposed variable selection procedure.     

MM Algorithms for Variance Components Models

Variance components estimation and mixed model analysis are central themes in statistics with applications in numerous scientific disciplines. Despite the best efforts of generations of statisticians and numerical analysts, maximum likelihood estimation (MLE) and restricted MLE of variance component models remain numerically challenging. Building on the minorization–maximization (MM) principle, this article presents a novel iterative algorithm for variance components estimation. Our MM algorithm is trivial to implement and competitive on large data problems. The algorithm readily extends to more complicated problems such as linear mixed models, multivariate response models possibly with missing data, maximum a posteriori estimation, and penalized estimation. We establish the global convergence of the MM algorithm to a Karush–Kuhn–Tucker point and demonstrate, both numerically and theoretically, that it converges faster than the classical EM algorithm when the number of variance components is greater than two and all covariance matrices are positive definite. Supplementary materials for this article are available online.     

Ⅱ型区间删失数据下广义部分线性模型的筛极大似然估计(英文)

This article concerded with a semiparametric generalized partial linear model （GPLM） with the type Ⅱ censored data. A sieve maximum likelihood estimator （MLE） is proposed to estimate the parameter component, allowing exploration of the nonlinear relationship between a certain covariate and the response function. Asymptotic properties of the proposed sieve MLEs are discussed. Under some mild conditions, the estimators are shown to be strongly consistent. Moreover, the estimators of the unknown parameters are asymptotically normal and efficient, and the estimator of the nonparametric function has an optimal convergence rate.     

上市公司信用风险分析模型中的变量选择

当前上市公司信用风险数据所呈现出的高维度以及高相关性的特点严重影响了信用风险模型的准确性。为此本文结合已有算法以及信用风险模型的特点设计了一种新的基于非参数的变量选择方法。通过该方法对上市公司用风险相关变量进行分析筛选可以消除数据集中包含的噪声变量以及线性相关变量。本文同时还针对该方法设计了高变量维度下最优解求解算法。文章以Logistic模型为例对上市公司信用风险做了实证分析,研究结果表明与以往的变量选择方法相比该方法可以有效的降低数据维度,消除变量间的相关性,并同时提高模型的可靠性和预测精度。     

Annealed Importance Sampling for Ising Models with Mixed Boundary Conditions

This note introduces a method for sampling Ising models with mixed boundary conditions. As an application of annealed importance sampling and the Swendsen-Wang algorithm, the method adopts a sequence of intermediate distributions that keeps the temperature fixed but turns on the boundary condition gradually. The numerical results show that the variance of the sample weights is relatively small.     

混合模型中方差分量估计的容许性及非负估计

对含有两个方差分量的线性混合模型, 本文构造了方差分量的一个线性估计类, 它包含许多常见的方差分量估计. 在这个类中我们建立了容许性的必要条件, 据此得到了两个新的改进估计. 最后我们讨论了方差分量的非负估计, 得到了优于方差分析估计和Tatsuya估计的正估计.     

未决赔款准备金评估的对数正态模型及其预测分布的Bootstrap实现

目前在我国精算实务中对未决赔款准备金评估的不确定性风险逐渐重视,对不确定性加以度量显得很有必要.在以往关于未决赔款准备金的不确定性研究中,大多集中于预测均方误差.从数值角度看,如果应用随机模拟的方法,能得到未决赔款准备金完整的预测分布,那么就可以由该分布得到各个分位数以及相关的分布度量,对准备金负债评估的准确性和充足性具有重要的参考价值.研究的对数正态模型是未决赔款准备金评估中的分布模型之一,它假设累计赔款单个进展因子服从对数正态分布,进而将参数Bootstrap方法和非参数Bootstrap方法应用于对数正态模型中,得到了未决赔款准备金的预测分布,并通过精算实务中的数值实例加以实证分析.数值实例由当前国际上日益流行的统计软件R加以实现.     

我国宏观经济非参数联立模型的局部线性工具变量变窗宽估计

联立方程模型在经济政策制定、经济结构分析和预测方面起重要作用，目前关于非参数计量经济模型的研究主要停留在单方程模型上，而联立方程模型的研究在国际上刚刚起步，本将非参数回归模型的局部线性估计方法与传统联立方程模型估计方法相结合，首次提出了非参数计量经济联立模型的局部线性工具变量变窗宽估计并应用于我国宏观经济非参数联立模型，结果表明：我国宏观经济非参数联立模型优于线性联立模型且线性模型将造成不必要的人为设定误差；对于非参数联立模型，局部线性工具变量变窗宽估计优于局部线性估计。     

Reparameterized and Marginalized Posterior and Predictive Sampling for Complex Bayesian Geostatistical Models

This article proposes a four-pronged approach to efficient Bayesian estimation and prediction for complex Bayesian hierarchical Gaussian models for spatial and spatiotemporal data. The method involves reparameterizing the covariance structure of the model, reformulating the means structure, marginalizing the joint posterior distribution, and applying a simplex-based slice sampling algorithm. The approach permits fusion of point-source data and areal data measured at different resolutions and accommodates nonspatial correlation and variance heterogeneity as well as spatial and/or temporal correlation. The method produces Markov chain Monte Carlo samplers with low autocorrelation in the output, so that fewer iterations are needed for Bayesian inference than would be the case with other sampling algorithms. Supplemental materials are available online.     

强混合样本下随机设计情形线性模型的经验似然推断

本文研究强混合样本下随机设计情形线性模型的经验似然推断,将分块技术应用到经验似然方法中,证明了线性模型的参数β的对数经验似然比统计量的渐近分布为卡方分布,由此构造了强混合样本下β的经验似然置信区间.在有限样本情况下给出数值模拟结果.     

具有相关误差的多元线性模型的更新方程

已知的线性模型的更新方程是在对模型加了不相关误差结构的约束, 或只对带有固定参数的一元线性模型考虑的. 本文考虑具有相关误差的多元线性模型下的更新方程, 给出了在补充参数, 数据或指标时, 未知参数阵的最佳线性无偏估计及残积阵的更新方程. 公式适用于固定参数与随机参数两种情形.     

抽样调查中多个指标值的同时估计

本文在ＥＶ（Ｅｒｏｒ ｉｎ Ｖａｒｉａｂｌｅｓ）超总体模型下讨论有限总体两个指标值的同时估计问题，证明了样本均值作为整体均值的估计在均方差阵最小的意义下是最优的．     

具有外生变量部分线性自回归模型的样条估计

考虑自回归模型Y_t=θ~TX_t g(Zt) ε_t,t=1,…,n,其中X_t=(Y_(t-1),…,Y_(t-d))~T,Z_t为实值外生随机变量,θ=(θ_1,…,θ_d)~T为待估参数向量,g为未知非参数光滑函数．基于多项式样条方法,在一定的条件下,给出了θ的估计的渐近正态性,得到了g的估计的收敛速度．模拟例子验证了所得的理论结果．