Test for Heteroscedasticity in Partially Linear Regression Models

Testing heteroscedasticity determines whether the regression model can predict the dependent variable consistently across all values of the explanatory variables. Since the proposed tests could not detect heteroscedasticity in all cases, more precisely in heavy-tailed distributions, the authors established new comprehensive test statistic based on Levene's test. The authors built the asymptotic normality of the test statistic under the null hypothesis of homoscedasticity based on the recent theory of analysis of variance for the infinite factors level. The proposed test uses the residuals from a regression model fit of the mean function with Levene's test to assess homogeneity of variance. Simulation studies show that our test yields better than other methods in almost all cases even if the variance is a nonlinear function. Finally, the proposed method is implemented through a real data-set.     

周期异方差时间序列中季节单位根的Gauss 检验

为检验带异方差的季节时间序列中的单位根,提出了基于Cauchy估计的Zc统计量.在原假设下得到该检验统计量的极限分布服从于标准正态分布,并与季节周期d和误差项的周期异方差无关.Monte Carlo模拟计算表明当模型中自回归系数接近1时,用该方法得到的估计值比普通的最小二乘方法得到的估计值误差小.计算结果和实例分析结果表明了用该方法检验季节单位根的简便性和有效性.     

非线性时间序列建模的混合GARCH方法

在文献[1]的基础上，首次提出混合广义自回归务件异方差（Mixture Generalized Autoregressive Conditional Heteroscedastic Model简记MGARCH）模型；给出并证明了MGARCH模型的一阶平稳性的充分必要条件及二阶平稳性的充分务件；给出该模型参数估计的EM算法：利用BIC定阶准则对MGARCH模型的各成份进行定阶；计算结果表明该模型对金融非线性时间序列中存在的变异率现象具有较强的描述能力，有广阔的应用前景。     

一元线性模型异方差的局部多项式回归

本文将局部多项式回归的非参数方法用于线性模型中异方差的估计 ,改进了传统的两阶段法 ,得到了估计的一致性和渐近正态性 ,为探讨估计的有限样本性 ,给出了若干模拟的例子。     

基于分层贝叶斯模型的稳健参数设计

针对双响应曲面模型的参数不确定性、参数之间的层次结构以及模型的异方差问题,结合分层贝叶斯建模方法提出一种新的均值-方差双响应曲面模型,并在此基础上运用所提方法实现了产品/过程的稳健参数设计。首先,建立分层贝叶斯模型,并获得参数的后验分布;其次利用Gibbs采样获得参数估计值,在此基础上构建质量损失函数,并采用遗传算法对质量损失函数进行优化求得可控因子的最佳设计水平;最后,从模型具有同方差和异方差两种情形出发,结合具体实例分别采用普通最小二乘、加权最小二乘及分层贝叶斯建立双响应曲面模型进行了比较分析,验证了所提方法的有效性。     

Nonparametric regression under double-sampling designs

This paper studies nonparametric estimation of the regression function with surrogate outcome data under double-sampling designs, where a proxy response is observed for the full sample and the true response is observed on a validation set. A new estimation approach is proposed for estimating the regression function. The authors first estimate the regression function with a kernel smoother based on the validation subsample, and then improve the estimation by utilizing the information on the incomplete observations from the non-validation subsample and the surrogate of response from the full sample. Asymptotic normality of the proposed estimator is derived. The effectiveness of the proposed method is demonstrated via simulations.     

中值无偏修正的预白化HAC法在伪回归中的应用

传统预白化HAC法存在有限样本偏差,使得平稳过程之间回归时存在伪回归.本文利用一阶和高阶的中值无偏估计对传统预白化HAC进行修正,通过修正的预白化HAC对t统计量标准误进行调整,从而进一步研究其在平稳过程之间伪回归中的适用性.研究表明:第一,中值无偏修正的预白化HAC法能减少长期方差的有限样本偏差并能降低伪回归概率;第二,修正的预白化HAC对持久性、GARCH类异方差和非正态随机新息等都具有稳健性.     

Single-Index Quantile Regression with Left Truncated Data

The purpose of this paper is two fold. First, the authors investigate quantile regression(QR) estimation for single-index QR models when the response is subject to random left truncation.The random weights are introduced to deal with left truncated data and the associated iteration estimation method is proposed. The asymptotic properties for the proposed QR estimates of the index parameter and unknown link function are both obtained. Further, by combining the QR loss function and the adaptive LA...     

Variable selection for recurrent event data with informative censoring

Recurrent events data with a terminal event(e.g.,death) often arise in clinical and observational studies.Variable selection is an important issue in all regression analysis.In this paper, the authors first propose the estimation methods to select the significant variables,and then prove the asymptotic behavior of the proposed estimator.Furthermore,the authors discuss the computing algorithm to assess the proposed estimator via the linear function approximation and generalized cross validation method for determination of the tuning parameters.Finally,the finite sample estimation for the asymptotical covariance matrix is also proposed.     

Convergence of Self-Tuning Regulators under Conditional Heteroscedastic Noises with Unknown High-Frequency Gain

In the classical theory of self-tuning regulators, it always requires that the conditional variances of the systems noises are bounded. However, such a requirement may not be satisfied when modeling many practical systems, and one significant example is the well-known ARCH(autoregressive conditional heteroscedasticity) model in econometrics. The aim of this paper is to consider self-tuning regulators of linear stochastic systems with both unknown parameters and conditional heteroscedastic noises, where the adaptive controller will be designed based on both the weighted least-squares algorithm and the certainty equivalence principle. The authors will show that under some natural conditions on the system structure and the noises with unbounded conditional variances, the closed-loop adaptive control system will be globally stable and the tracking error will be asymptotically optimal.Thus, this paper provides a significant extension of the classical theory on self-tuning regulators with expanded applicability.     

Consistent Estimation of Order for Regression in the Presence of Serial Correlation and Heteroscedasticity

1　 IntroductionIn econometric regression models,serial correlation and heteroscedasticity are twocommonly encoutered features of disturbance behavior and may often occursimultaneously. For example,the effects of omitted variables may give rise to bothproblems simultaneously.Therefore,an econometric regression model where both serialcorrelation and heteroscedasticity are present would be feasible.There are literaturesconcerning efficient estimation of regressions with both serial correlation a…     

Robust Estimation for Partial Functional Linear Regression Model Based on Modal Regression

This paper presents a robust estimation procedure by using modal regression for the partial functional linear regression, which combines the common linear model with the functional linear regression model. The outstanding merit of the new method is that it is robust against outliers or heavy-tail error distributions while performs no worse than the least-square-based estimation method for normal error cases. The slope function is fitted by B-spline. Under suitable conditions, the authors obtain the convergence rates and asymptotic normality of the estimators. Finally, simulation studies and a real data example are conducted to examine the finite sample performance of the proposed method. Both the simulation results and the real data analysis confirm that the newly proposed method works very well.     

Box—Cox变换模型模拟矩估计方法研究

基于对Ｂｏｘ－Ｃｏｘ变换模型参数估计方法的研究，分析了极大似然方法之不足，提出了相应参数估计方法——基于残差的广义模拟矩估计方法，并研制了相应的算法．该方法同时解决了Ｂｏｘ－Ｃｏｘ变换模型出现序列相关与异方差时的参数估计问题，力求反映真实模型．算例则验证了方法的有效性     

自助法修正的预白化HAC在平稳过程之间伪回归中的应用

传统预白化HAC法存在有限样本偏差,导致在相互独立的平稳过程回归中存在较高的伪回归概率.本文提出利用自助法减少其有限样本偏差,从而降低伪回归概率.同时,通过与一阶偏差修正的预白化HAC法的比较分析,发现自助法修正的预白化HAC不仅能减少偏差和降低伪回归概率,而且在GARCH类异方差和高阶自回归数据过程中具有更好的稳健性.     

基于多智能体导航的高超飞行器信息融合方法

精确导航技术是高超飞行器(hypersonic vehicle, HV)充分发挥威力的关键所在。然而,高马赫数和强机动性致使HV的导航系统误差及其观测噪声无法准确描述,制约着导航信息的精确性和实时性。为及时获取高精度导航信息,设计基于集员框架的卡尔曼滤波算法。一方面采用多智能体分布式协同探测,形成观测椭球交叉集合,提高了观测效率和测量精度;另一方面,通过设计两类噪声模型,求其与状态估值的最小均方误差,实现滤波增益的计算,提高算法对噪声的抗扰动能力,使状态估值达到均方误差最小。通过数字仿真,将设计方法应用到HV导航模型中,并与扩展卡尔曼滤波和集员滤波的状态估值进行比较,结果表明,提出算法在不同噪声影响下具有更高的估计精度。研究成果将为HV实现实时精确导航提供技术支持,并具有重要的理论意义和应用价值。     

Double Penalized Semi-Parametric Signed-Rank Regression with Adaptive LASSO

In this paper, a semi-parametric regression model with an adaptive LASSO penalty imposed on both the linear and the nonlinear components of the mode is considered. The model is rewritten so that a signed-rank technique can be used for estimation. The nonlinear part consists of a covariate that enters the model nonlinearly via an unknown function that is estimated using Bsplines. The author shows that the resulting estimator is consistent under heavy-tailed distributions and asymptotic normality results are given. Monte Carlo simulations as well as practical applications are studied to assess the validity of the proposed estimation method.     

LS-SVM与多层前向网络的非线性回归性能比较

在阐述支持向量机(SVM)和最小二乘支持向量机(LS-SVM)的原理并比较了两者的优缺点后,将LS-SVM与多层前向网络中的两种典型网络BP网络和RBF网络,分别应用于装载机载重动态测量的非线性函数回归估计中,对这三种网络在函数逼近和泛化能力两方面的性能进行比较研究。仿真结果表明,LS-SVM在精度和泛化性能两方面做到了最好的折衷,是用于非线性函数回归分析的一种很有效的方法。     

异方差下条件处理效应的非参数估计及应用

处理效应模型作为分析政策效应的量化工具,在社会与经济的各个领域有着广泛的应用.现有文献为了得到处理效应的一致估计量,通常需要加一些较强的限制条件(如条件均值独立)或采用工具变量法,在较弱的与实际更吻合的条件下给出处理效应的一致估计量并不多见.本文在误差项对称的假定下,讨论了条件处理效应模型的非参数识别和估计,并进一步估计了平均处理效应.我们通过放松模型中函数形式的假定,同时考虑了较为普遍的广义异方差形式,大大减少了模型误设的可能性,拓展了现有模型的适用性.本文对估计量的大样本性质进行了分析,表明了估计量的一致性和渐近正态性,蒙特卡罗模拟显示了估计量良好的有限样本性质.最后,本文将估计量应用于研究大学教育回报及其性别差异,进一步解释了估计量的实用价值.     

窄带雷达高速多目标检测及其运动参数估计

提出一种窄带雷达高速多目标检测和运动参数估计方法。利用"Dechirping"加滤波的方法对回波进行分离并且构造每个目标的回波矩阵,接着对每个回波矩阵构造相干相位补偿函数并进行补偿,从而实现信号能量的相干积累,有利于目标的检测。利用估计的多普勒信息可以获得目标的运动参数。仿真结果验证了所提方法的有效性。     

Blind channel estimation for redundant precoded OFDM systems

Considering that channel estimation can play a crucial role in coherent detection of the information symbols in each data block, a blind channel estimation approach is proposed for redundant precoded orthogonal frequency-division multiplexing (OFDM) systems. A redundant linear frequency-domain precoder is applied to each pair of blocks before they enter the OFDM system. Because of the introduced structure, the frequency-selective channel can be identified at the receiver based on autocorrelation operations, singular value decomposition (SVD), and by resolving the scalar ambiguity. The proposed channel estimation method has low computational complexity and requires no prior statistical information on channel or noise. And the proposed blind method has high spectral efficiency owing to exploiting no training sequence. Computer simulations confirm that this proposed blind channel estimation method can identify the frequency-selective channels perfectly and obtain a good performance.