Absolute penalty and shrinkage estimation in partially linear models

In the context of a partially linear regression model, shrinkage semiparametric estimation is considered based on the Stein-rule. In this framework, the coefficient vector is partitioned into two sub-vectors: the first sub-vector gives the coefficients of interest, i.e., main effects (for example, treatment effects), and the second sub-vector is for variables that may or may not need to be controlled. When estimating the first sub-vector, the best estimate may be obtained using either the full model that includes both sub-vectors, or the reduced model which leaves out the second sub-vector. It is demonstrated that shrinkage estimators which combine two semiparametric estimators computed for the full model and the reduced model outperform the semiparametric estimator for the full model. Using the semiparametric estimate for the reduced model is best when the second sub-vector is the null vector, but this estimator suffers seriously from bias otherwise. The relative dominance picture of suggested estimators is investigated. In particular, suitability of estimating the nonparametric component based on the B-spline basis function is explored. Further, the performance of the proposed estimators is compared with an absolute penalty estimator through Monte Carlo simulation. Lasso and adaptive lasso were implemented for simultaneous model selection and parameter estimation. A real data example is given to compare the proposed estimators with lasso and adaptive lasso estimators.     

改进的GMPF在动态跟踪系统中的应用

针对动态跟踪系统的非线性问题,提出一种非线性非高斯性能较优的状态估计器:考虑最新观测值的影响,使用基于平方根二阶差分(SRDD2)的高斯混合(GM)模型给出粒子滤波的建议分布.重要性采样和再采样阶段分别采用基于蒙特卡罗的重要性采样和进化的再采样方法,以减轻粒子滤波(PF)的样本退化问题,增强样本的多样性.实验结果表明,与平方根二阶差分Kalman滤波、PF、GM粒子滤波相比,该状态估计器提高了动态跟踪系统状态估计器的综合估计性能.     

隐私团校准的模糊MEB学习

在一定条件下,基于最小累积平方误差（ISE）准则的高斯核密度估计与最小包含球（MEB）等价.在此基础上提出了一种含团状隐私数据保护的MEB学习方法,称为隐私团校准的MEB（PCC-MEB）方法;同时,通过引入模糊隶属度函数将PCC-MEB拓展为模糊的PCC-MEB（FPCC-MEB）,从而解决二类及多类问题中区域不可分问题.人造和真实数据集上的实验结果表明,所提出方法具有较好的性能.     

一种多径线性调频信号的参数估计方法

基于循环互相关变换，提出了一种结合循环频率域的信号分离技术的多径LFM信号参数估计方法。该方法首先利用信号循环相关变换，估计出源信号的特征参数，根据估计出的参数重构出源信号，并将其和观测信号作循环互相关变换，再根据信号循环频率域上的多径信号特征，估计出最强路径信号分量的时延和衰减因子参数，然后重构最强路径分量，最后将其从观测信号中滤去，这样逐次分离出各径信号，从而有效地抑制了强路径信号分量对弱路径信号分量的影响。最后运用计算机进行了仿真实验。     

Relative hazard rate estimation for right censored and left truncated data

In medical research differences among treatment groups are a common focus of study. The concept of relative hazard rate is a tool for comparing two groups in terms of their difference in risk rates. A kernel estimator is proposed in the case where both samples are subject to left truncation and right censoring and an iid representation is obtained in this setup. The asymptotic distribution and the asymptotic mean squared error of the estimator are obtained. An application to the famous Channing House data set illustrates the theory. Research supported in part by MCyT Grants PB98-0182-C02-01 and BFM2002-00265 (ERDF support included) and XUGA Grant PGIDT00PX110501 PN for the first author and by the Ministry of the Flemish Community (Project BIL00/28, International Scientific and Technological Cooperation) and by the IAP research network nr P5/24 of the Belgian State (Federal Office for Scientific, Technical and Cultural Affairs) for the last two authors.     

Robust estimation of continuous nonlinear plants with discrete measurements

In this work the problem of designing a state estimator for completely or partially observable continuous nonlinear plants with discrete measurements is addressed. The combination of a geometric approach with a stability analysis yields an estimator design methodology with a nonlinear detectability condition susceptible of testing, a systematic estimator construction, a robust convergence criterion coupled with a simple tuning scheme, as well as a rationale to explain the interplay between sampling time, estimator gains, and estimator functioning. Comparing with the continuous measurement case where the convergence is attained by tuning the gain above a low limit, in the discrete measurement case the loss of information due to the measurement sampling increases the size of the lower gain limit, and imposes sampling time and high gain limits. The proposed methodology is applied to address the estimation problem of a class of solution homopolymerization reactors, and is tested with a methyl-methacrylate polymerization run taken from a previous extended Kalman filter implementation study with experimental data.     

Comparative analysis of different approaches to target differentiation and localization with sonar

This study compares the performances of different methods for the differentiation and localization of commonly encountered features in indoor environments. Differentiation of such features is of interest for intelligent systems in a variety of applications such as system control based on acoustic signal detection and identification, map building, navigation, obstacle avoidance, and target tracking. Different representations of amplitude and time-of-flight measurement patterns experimentally acquired from a real sonar system are processed. The approaches compared in this study include the target differentiation algorithm, Dempster-Shafer evidential reasoning, different kinds of voting schemes, statistical pattern recognition techniques (k-nearest neighbor classifier, kernel estimator, parameterized density estimator, linear discriminant analysis, and fuzzy c-means clustering algorithm), and artificial neural networks. The neural networks are trained with different input signal representations obtained using pre-processing techniques such as discrete ordinary and fractional Fourier, Hartley and wavelet transforms, and Kohonen's self-organizing feature map. The use of neural networks trained with the back-propagation algorithm, usually with fractional Fourier transform or wavelet pre-processing results in near perfect differentiation, around 85% correct range estimation and around 95% correct azimuth estimation, which would be satisfactory in a wide range of applications.     

Perturbation Analysis of Multiclass Stochastic Fluid Models

We use a stochastic fluid model (SFM) to capture the operation of finite-capacity queueing systems with multiple customer classes. We derive gradient estimators for class-dependent loss and workload related performance metrics with respect to any one of several threshold parameters used for buffer control. These estimators are shown to be unbiased and directly observable from a sample path without any knowledge of underlying stochastic characteristics of the traffic processes. This renders them computable in on-line environments and easily implementable in settings such as communication networks.     

A Note on Weighted Least‐squares Estimation of the Shape Parameter of the Weibull Distribution

This study proposes an alternative to the weighted least‐squares (WLS) procedure for estimating the shape parameter of the Weibull distribution. Bergman (Journal of Materials Science Letters 1986; 5:611–614), Faucher and Tyson (F&T) (Journal of Materials Science Letters 1988; 7:1199–1203) suggested using different WLS approaches for Weibull parameters. However, the simulation results show that the novel approach is better than that of Bergman, and is not significantly different from that of F&T. Furthermore, the novel approach is also simpler and easier to comprehend. Copyright © 2004 John Wiley & Sons, Ltd.     

Two guaranteed equilibrated error estimators for Harmonic formulations in eddy current problems

In this paper a guaranteed equilibrated error estimator is developed for the 3D harmonic magnetodynamic problem of Maxwell’s system. This system is recasted in the classical $\mathbf{A}?\phi$ potential formulation and solved by the Finite Element method. The error estimator is built starting from the $\mathbf{A}?\phi$ numerical solution by a local flux reconstruction technique. Its equivalence with the error in the energy norm is established. A comparison of this estimator with an equilibrated error estimator already developed through a complementary problem points out the advantages and drawbacks of these two estimators. In particular, an analytical benchmark test illustrates the obtained theoretical results and a physical benchmark test shows the efficiency of these two estimators.