进出口贸易对通货膨胀的影响模型分析

本文建立了进出口贸易对通货膨胀影响的半参数变系数部分线性模型,采用Profile最小二乘估计对模型参数进行了估计,并与简单的多元线性回归模型作比较。研究结果显示：半参数变系数部分线性模型更具有效性,预测能力也非常的优良,能更好的反映出进出口贸易与通货膨胀之间的关系。最后,利用研究结果并结合经济理论对抑制我国通货膨胀提出了建议与措施。     

基于函数型非参数方法的气温数据预测分析

基于函数型非参数核回归估计的方法分析安徽省1955年至2010年月度平均气温数据,建立函数型非参数回归模型,并对2010年气温数据进行实证研究.同时,与经典的非参数回归模型的预测结果相比,本文方法的预测均方误差明显优于经典的非参数回归方法,体现出函数型非参数模型的优越性.     

随机删失非参数回归模型的离差度量

主要研究了随机删失非参数固定设计回归模型的离差度量.首先利用随机删失非参数回归模型的性质和生存分布的Kaplan-Meier乘积限估计,将原模型转化为非参数回归模型进行研究;然后对模型作了离差度量分析,得到了模型的离差度量序列;最后通过实例分析,验证了上述诊断方法的有效性.     

人口增长率的非参数自回归预测模型

针对传统的人口增长预测模型不能理想地捕获我国人口增长率数据的非线性性特征,本文基于局部线性非参数估计理论,对我国建国以来的年人口增长率建立了非参数自回归NAR(1)模型,并对2000-2003年的年人口增长率进行了预测,计算结果表明,相对于参数自回归模型而言,非参数自回归模型能够很好地解决人口增长预测这一非线性问题,预测精度较高。     

有结构变化的半参数回归模型及其级数估计

结合半参数回归模型和含未知变点的结构变化模型,提出一个参数和非参数分量同时存在结构变化的新模型——有结构变化的半参数回归模型.在新模型非参数分量的级数估计基础上,得出模型参数的最小二乘估计,进一步推得条件期望函数估计的收敛速度及其渐近正态性.随机模拟结果表明,本文的新模型及估计方法具有广泛的适用性和灵活性.     

随机右删失非参数回归模型的影响分析

将随机删失非参数固定设计回归模型转化为非参数回归模型进行研究;然后对此模型作了局部影响分析,得到计算影响矩阵及最大影响曲率方向的简洁公式;最后通过实例分析,验证了分析方法的有效性.     

随机权函数半参数回归模型的导方差统计分析

在回归分析中,观测值的方差齐性只是一个基本的假定,在参数、半参数和非参数回归模型中关于异方差检验和估计问题已有很多研究.本文在冉昊和朱忠义(2004)讨论的半参数回归模型的基础上,用随机参数方法,讨论随机权函数半参数回归模型中的异方差检验问题,得到了方差齐性检验Score统计量,同时,当半参数模型存在异方差时,本文还给出了估计方差的方法.     

缺失数据下φ混合样本情形非参数回归函数的经验似然推断

在φ混合的随机误差下,本文研究了固定设计及响应变量有缺失的非参数回归模型中回归函数的经验似然置信区间的构造.首先采用非参数回归填补法对缺失的数据进行填补,其次利用补足后得到的"完全样本"构造了非参数回归函数的经验似然比统计量,并证明了经验似然比统计量的极限分布为卡方分布,利用此结果可以构造非参数回归函数的经验似然置信区间.     

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在φ混合的随机误差下,本文研究了固定设计及响应变量有缺失的非参数回归模型中回归函数的经验似然置信区间的构造.首先采用非参数回归填补法对缺失的数据进行填补,其次利用补足后得到的"完全样本"构造了非参数回归函数的经验似然比统计量,并证明了经验似然比统计量的极限分布为卡方分布,利用此结果可以构造非参数回归函数的经验似然置信区间.     

纵向数据下半参数回归模型的统计分析

对于纵向数据下半参数回归模型,基于广义估计方程和一般权函数方法构造了模型中参数分量和非参数分量的估计.在适当的条件下证明了参数估计量具有渐近正态性,并得到了非参数回归函数估计量的最优收敛速度.通过模拟研究说明了所提出的估计量在有限样本下的精确性.     

城镇居民非参数消费敏感度模型及应用——基于局部线性工具变量估计方法

建立了城镇居民非参数消费敏感度模型,该模型不需做任何形式假设,避免了线性及非线性模型的误设.采用局部线性工具变量方法对其进行估计,结果表明城镇居民消费敏感度是时变的,和居民收入变动保持同步,支持流动性约束假说.另外高通货膨胀时的负实际利率变动比低利率、温和通胀时造成的负实际利率变动对消费支出的冲击要大得多.因此在增加城镇居民收入,突破流动性约束瓶颈的同时,高度关注通货膨胀,使得其消费水平得到真实提高.     

Semiparametric Bayesian Regression via Potts Model

We consider Bayesian nonparametric regression through random partition models. Our approach involves the construction of a covariate-dependent prior distribution on partitions of individuals. Our goal is to use covariate information to improve predictive inference. To do so, we propose a prior on partitions based on the Potts clustering model associated with the observed covariates. This drives by covariate proximity both the formation of clusters, and the prior predictive distribution. The resulting prior model is flexible enough to support many different types of likelihood models. We focus the discussion on nonparametric regression. Implementation details are discussed for the specific case of multivariate multiple linear regression. The proposed model performs well in terms of model fitting and prediction when compared to other alternative nonparametric regression approaches. We illustrate the methodology with an application to the health status of nations at the turn of the 21st century. Supplementary materials are available online.     

固定设计下非参数回归模型估计的渐近性质

考虑固定设计下具有非参数AR(1)的非参数回归模型,综合最小二乘和非参数核估计法,定义了非参数函数的估计量,在适当的条件下,研究了它们的渐近性质.     

Estimating the error distribution in nonparametric multiple regression with applications to model testing

In this paper we consider the estimation of the error distribution in a heteroscedastic nonparametric regression model with multivariate covariates. As estimator we consider the empirical distribution function of residuals, which are obtained from multivariate local polynomial fits of the regression and variance functions, respectively. Weak convergence of the empirical residual process to a Gaussian process is proved. We also consider various applications for testing model assumptions in nonparametric multiple regression. The model tests obtained are able to detect local alternatives that converge to zero at an n^−1/2-rate, independent of the covariate dimension. We consider in detail a test for additivity of the regression function.     

A note on the Bayes factor in a semiparametric regression model

In this paper, we consider a semiparametric regression model where the unknown regression function is the sum of parametric and nonparametric parts. The parametric part is a finite-dimensional multiple regression function whereas the nonparametric part is represented by an infinite series of orthogonal basis. In this model, we investigate the large sample property of the Bayes factor for testing the parametric null model against the semiparametric alternative model. Under some conditions on the prior and design matrix, we identify the analytic form of the Bayes factor and show that the Bayes factor is consistent, i.e. converges to infinity in probability under the parametric null model, while converges to zero under the semiparametric alternative, as the sample size increases.     

一类线性回归模型中核估计的渐近性质

考虑固定设计下具有一阶非参数自回归误差的线性模型,构造了参数和非参数函数的N-W核估计,在适当的条件下,证明了参数估计的强相合性,同时给出了非参数函数估计的渐近正态性.     

Testing heteroscedasticity by wavelets in a nonparametric regression model

In the nonparametric regression models, a homoscedastic structure is usually assumed. However, the homoscedasticity cannot be guaranteed a priori. Hence, testing the heteroscedasticity is needed. In this paper we propose a consistent nonparametric test for heteroscedasticity, based on wavelets. The empirical wavelet coefficients of the conditional variance in a regression model are defined first. Then they are shown to be asymptotically normal, based on which a test statistic for the heteroscedasticity is constructed by using Fan's wavelet thresholding idea. Simulations show that our test is superior to the traditional nonparametric test.     

误差为鞅差序列的回归函数估计的收敛速度

当误差为鞅差序列时,研究固定设计点列情形下非参数回归函数一般权函数的非参数估计,并在一些基本条件下给出了估计的一致最优强收敛速度．     

Asymptotic properties of posterior distributions in nonparametric regression with non-Gaussian errors

We investigate the asymptotic behavior of posterior distributions in nonparametric regression problems when the distribution of noise structure of the regression model is assumed to be non-Gaussian but symmetric such as the Laplace distribution. Given prior distributions for the unknown regression function and the scale parameter of noise distribution, we show that the posterior distribution concentrates around the true values of parameters. Following the approach by Choi and Schervish (Journal of Multivariate Analysis, 98, 1969–1987, 2007) and extending their results, we prove consistency of the posterior distribution of the parameters for the nonparametric regression when errors are symmetric non-Gaussian with suitable assumptions.