Heckman-Tobit模型的半参数估计

Heckman-Tobit模型可以同时处理样本选择问题和删失数据问题,是一个重要的微观计量模型.本文根据结果变量的条件生存函数所满足的性质,提出Heckman-Tobit模型的一种半参数估计方法.这种方法通过积分的形式,有效地利用了结果变量整个条件分布的信息.在一些正则性条件下,本文证明了所提出的半参数估计量的相合性和渐近正态性.其渐近性质的成立不依赖于扰动项的具体分布.数值模拟实验的结果表明,本文的半参数估计量具有优越的有限样本性质,且当扰动项服从非正态分布时优于最大似然估计量.     

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.     

Estimation in Linear Regression with Laplace Measurement Error Using Tweedie-Type Formula

Based on a Tweedie-type formula developed under the Laplace distribution, this paper proposes a new bias-corrected estimator of the regression parameters in a simple linear model when the measurement error follows a Laplace distribution. Large sample properties, including the consistency and the asymptotic normality, are investigated. The finite sample performance of the proposed estimators are evaluated via simulation studies, as well as comparison studies with some existing estimation procedures.     

线性回归模型的非线性稳健估计——基于经验过程理论的研究

线性回归模型参数估计的有效性及对厚尾扰动和离群值的稳健性有进一步改进的余地.本文基于条件分布函数提出线性参数模型的一种新的非线性稳健估计量,利用经验过程理论证明了其相合性和渐近正态性.相对于OLS(ordinary least squares)估计量和常用的稳健LAD(least absolute deviations)和Huber估计量,此估计量可全面把握因变量的分布信息,较准确地由样本数据反映真正的数据生成过程,关于扰动项的厚尾分布具有更好的稳健性,且可更好地消弱极端离群值样本对参数估计的不良影响.多种实验设计的模拟表明,此估计量在有限样本下表现良好;在厚尾扰动或离群值出现的时候,显示出良好的稳健性,且优于OLS、LAD以及Huber估计量的小样本表现.     

基于L型级联互素阵列的多目标源载频和到达角联合估计

随着通信频段的不断拓展，受到Nyquist意义下的阵列间隔和采样频率的限制，多带信号载频和到达角的联合估计难度急剧增大。为突破该瓶颈，设计了一种基于L型级联互素阵列的空时欠采样接收结构，并结合伪噪声空间重构、信源相关性配对，提出了一种全解析的载频和到达角的配对和无模糊联合估计方法。得益于稀疏阵列和时域欠采样，所提结构有效增大了阵列孔径并降低了采样成本。仿真实验证明了所提载频和到达角联合估计方法在较低快拍数和信噪比的条件下，可取得较高的估计精度。     

Profile Statistical Inference for Partially Linear Additive Models with a Diverging Number of Parameters

This paper considers partially linear additive models with the number of parameters diverging when some linear constraints on the parametric part are available. This paper proposes a constrained profile least-squares estimation for the parametric components with the nonparametric functions being estimated by basis function approximations. The consistency and asymptotic normality of the restricted estimator are given under some certain conditions. The authors construct a profile likelihood ratio test statistic to test the validity of the linear constraints on the parametric components,and demonstrate that it follows asymptotically chi-squared distribution under the null and alternative hypotheses. The finite sample performance of the proposed method is illustrated by simulation studies and a data analysis.     

具有内生性的随机变系数模型的PLS估计

在随机变系数模型中,自变量很可能与系数方程中的随机斜率相关,从而引起内生性问题.这样的内生性同样会导致标准的OLS (ordinary least squares)估计量有偏且非一致.本文通过非参方法来解决这种内生性问题.首先设定此种相关性为一个未知方程,将其直接加入模型,再利用PLS (profile least squares)方法进行估计.在给出确定性参数的渐近性质后,本文比较了PLS和OLS估计量的有限样本性质.Monte Carlo结果显示,一般而言,PLS方法都优于OLS方法.最后,本文以社会经济地位对学生成绩的影响为例,考察了PLS和OLS估计值的区别.     

基于随机模拟的资产组合有效子集的精确检验方法

考虑到资产组合有效子集的渐近检验方法通常只适用于大样本情形，为了提高在有限样本和小样本情形下有效子集判定的准确性和稳定性，通过分析资产组合有效子集新的判定条件，提出了一种基于Block-Bootstrap随机模拟的有效子集精确检验方法，同时对几类一致性协方差阵估计下的稳健检验方法进行了检验功效和检验水平的比较，模拟结果表明基于Block-Bootstrap随机模拟的精确检验方法更为可靠，实证结果也表明该方法得到的有效子集更为稳定.     

基于Metropolis Hastings抽样短采样宽带信号方位估计AML算法

针对短采样宽带信号近似最大似然（approximated maximum likelihood,AML）方位估计计算量大的问题,将马尔科夫链〖CD*2〗蒙特卡罗方法与近似最大似然方位估计相结合,提出一种基于Metropolis Hastings抽样的近似最大似然方位估计方法（AMLMH）。该方法将AML算法的空间谱函数作为信号的概率分布函数,并利用Metropolis Hastings抽样方法从该概率分布函数中抽样。研究结果表明,AMLMH方法不但保持了原近似最大似然方位估计方法的优良性能,而且减小了计算量。     

Smoothed estimator of quantile residual lifetime for right censored data

It is of great interest to estimate quantile residual lifetime in medical science and many other ?elds. In survival analysis, Kaplan-Meier(K-M) estimator has been widely used to estimate the survival distribution. However, it is well-known that the K-M estimator is not continuous, thus it can not always be used to calculate quantile residual lifetime. In this paper, the authors propose a kernel smoothing method to give an estimator of quantile residual lifetime. By using modern empirical process techniques, the consistency and the asymptotic normality of the proposed estimator are provided neatly.The authors also present the empirical small sample performances of the estimator. De?ciency is introduced to compare the performance of the proposed estimator with the naive unsmoothed estimator of the quantile residaul lifetime. Further simulation studies indicate that the proposed estimator performs very well.     

FINITE TIME TRACKING CONTROL FOR RIGID ROBOTIC MANIPULATORS WITH FRICTION AND EXTERNAL DISTURBANCES

1.Introduction Variable structure control is well known for its robustness to system uncertainties and external disturbances and has been successfully applied to the rigid robotic manipulator systems(Utkin(1992),Zinober(1990)).Generally,a linear sliding mode is firstly designed to describe the desired system error dynamics,a robust controller drives the switching plane variables to reach the sliding mode,and the error dynamics will asymptotically converge to zero on the linear sliding mode.Si…     

一种基于吉布斯抽样的MUSIC频率-方位联合估计算法

将马尔可夫蒙特卡罗方法与MUSIC方法估计相结合,提出一种基于吉布斯抽样的频率-方位联合估计新方法(MUSIC FREQ-DOA joint Estimator Based on Gibbs Sampling,简称Gibbs-MUSIC).并用该方法联合估计多个目标的频率和方位.这种方法将MUSIC方法的谱函数作为频率和方位的联合概率密度函数,并采用马尔可夫蒙特卡罗(MCMC)吉布斯抽样方法对该联合概率密度函数进行采样.仿真实验显示在目标个数较少时,该方法不仅保持了常规MUSIC方法的高分辨能力,而且降低了计算量,减少了存储量.     

Equivalent Time-Domain Representation and Uplink Channel Estimator for Multicarrier CDMA Systems

Many blind channel estimation methods have been proposed for direct sequence (DS) code-division multiple access (CDMA) systems, so we can certainly use them to estimate the finite impulse response (FIR) channel for the multi-carrier (MC-) CDMA system. In this paper, the MC-CDMA system is interpreted as an equivalent time-domain DS-CD-MA system with specific spreading codes. Then, an equivalently time-domain blind channel estimator is derived for the     

具有截面相关的变系数面板数据模型的估计及应用

本文建立了一类变系数面板数据模型.此模型假设自变量的系数是某一平滑变量的未知函数,允许自变量,平滑变量和误差项存在通过共同因子结构引入的截面相关.由于一些共同因子的不可观测性,本文采用局部线性共同相关效应估计方法对未知的函数进行估计并给出了估计量的渐近性质.蒙特卡罗模拟结果表明该估计方法具有良好的小样本性质.利用1990-2012年中国省级面板数据,本文对我国外商直接投资与经济增长之间的关系进行了实证分析,结果表明:外商直接投资和经济增长之间存在明显非线性关系,各省份不同的初始经济水平会导致外商直接投资对经济增长的影响不同.     

H∞ fault estimation for a class of linear time-delay systems in finite frequency domain

This paper deals with the problem of H∞ fault estimation for linear time-delay systems in finite frequency domain. First a generalized coordinate change is applied to the original system such that in the new coordinates all the time-delay terms are injected by the system's input and output. Then an observer-based H∞ fault estimator with input and output injections is proposed for fault estimation with known frequency range. With the aid of Generalized Kalman-Yakubovich-Popov lemma, sufficient conditions on the existence of the H∞ fault estimator are derived and a solution to the observer gain matrices is obtained by solving a set of linear matrix inequalities. Finally, a numerical example is given to illustrate the effectiveness of the proposed method.     

Periodic performance of the chaotic spread spectrum sequence on finite precision

It is well known that the periodic performance of spread spectrum sequence heavily affects the correlative and secure characteristics of communication systems.The chaotic binary sequence is paid more and more attention since it is one kind of applicable spread spectrum sequences.However,there are unavoidable short cyclic problems for chaotic binary sequences in finite precision.The chaotic binary sequence generating methods are studied first.Then the short cyclic behavior of the chaotic sequences is analyzed in detail,which are generated by quantification approaches with finite word-length.At the same time,a chaotic similar function is defined for presenting the cyclic characteristics of the sequences.Based on these efforts,an improved method with scrambling control for generating chaotic binary sequences is proposed.To quantitatively describe the improvement of periodic performance of the sequences,an orthogonal estimator is also defined.Some simulating results are provided.From the theoretical deduction and the experimental results,it is concluded that the proposed method can effectively increase the period and raise the complexity of the chaotic sequences to some extent.     

Periodic performance of the chaotic spreadspectrum sequence on finite precision

It is well known that the periodic performance of spread spectrum sequence heavily affects the correlative and secure characteristics of communication systems. The chaotic binary sequence is paid more and more attention since it is one kind of applicable spread spectrum sequences. However, there are unavoidable short cyclic problems for chaotic binary sequences in finite precision. The chaotic binary sequence generating methods are studied first. Then the short cyclic behavior of the chaotic sequences is analyzed in detail, which are generated by quantification approaches with finite word-length. At the same time, a chaotic similar function is defined for presenting the cyclic characteristics of the sequences. Based on these efforts, an improved method with scrambling control for generating chaotic binary sequences is proposed. To quantitatively describe the improvement of periodic performance of the sequences, an orthogonal estimator is also defined. Some simulating results are provided. From the theoretical deduction and the experimental results, it is concluded that the proposed method can effectively increase the period and raise the complexity of the chaotic sequences to some extent.     

基于虚拟失效序列样本的系统实时可靠性研究

针对一类不能获得足够数量的试验和现场使用数据的复杂系统,提出Monte Carlo(MC)实时可靠性估计方法.以系统部件失效时间序列作为样本空间,应用MC法仿真产生更多的虚拟样本,建立了虚拟样本序列下系统实时可靠性估计的一般框架,并研究了MC估计的期望和方差与先验信息之间的关系,得到了具有包含关系的信息条件下,基于较少先验信息的估计的方差不超过拥有更多先验信息的估计的方差.考虑所有部件失效时间序列和导致系统失效的部分部件失效时间序列的先验信息,分别导出了系统实时可靠性模型.最后,通过算例分析,验证了本文不同先验信息下可靠性模型的有效性和方法的合理性.     

H∞ fault estimation for a class of linear time-delay systems in finite frequency domain

This paper deals with the problem of H∞ fault estimation for linear time-delay systems in finite frequency domain. First a generalized coordinate change is applied to the original system such that in the new coordinates all the time-delay terms are injected by the system’s input and output. Then an observer-based H∞ fault estimator with input and output injections is proposed for fault estimation with known frequency range. With the aid of Generalized Kalman-Yakubovich-Popov lemma, sufficient conditions on the existence of the H∞ fault estimator are derived and a solution to the observer gain matrices is obtained by solving a set of linear matrix inequalities. Finally, a numerical example is given to illustrate the effectiveness of the proposed method.     

基于Kriging模型与重要性抽样的可靠性灵敏度分析

针对因无法获得功能函数的梯度信息而不能使用解析方法的情形,提出了进行可靠性灵敏度分析的高效的仿真方法,首先基于Kriging模型和重要性抽样去计算失效概率,然后通过记分函数(score function)方法求出失效概率对各个参数的偏导数。在计算失效概率时采用反问题(inversion problems)中的不确定性逐步减少(stepwise uncertainty reduction)准则来更新功能函数的Kriging模型,继而在重要性抽样的框架下将失效概率表示成一个"增大"的失效概率与修正项的乘积;而记分函数方法只是对前面抽样方法的一个简单后处理,不需要计算额外的功能函数值.对所提方法使用算例验证表明:当功能函数为昂贵的计算模型或对系统(非单个构件)进行灵敏度分析时,该方法具有较高的计算效率和精度。