A Non‐Parametric Estimator of the Spectral Density of a Continuous‐Time Gaussian Process Observed at Random Times

Abstract. In numerous applications data are observed at random times and an estimated graph of the spectral density may be relevant for characterizing and explaining phenomena. By using a wavelet analysis, one derives a non‐parametric estimator of the spectral density of a Gaussian process with stationary increments (or a stationary Gaussian process) from the observation of one path at random discrete times. For every positive frequency, this estimator is proved to satisfy a central limit theorem with a convergence rate depending on the roughness of the process and the moment of random durations between successive observations. In the case of stationary Gaussian processes, one can compare this estimator with estimators based on the empirical periodogram. Both estimators reach the same optimal rate of convergence, but the estimator based on wavelet analysis converges for a different class of random times. Simulation examples and an application to biological data are also provided.     

Functional logistic regression: a comparison of three methods

Functional logistic regression is becoming more popular as there are many situations where we are interested in the relation between functional covariates (as input) and a binary response (as output). Several approaches have been advocated, and this paper goes into detail about three of them: dimension reduction via functional principal component analysis, penalized functional regression, and wavelet expansions in combination with Least Absolute Shrinking and Selection Operator penalization. We discuss the performance of the three methods on simulated data and also apply the methods to data regarding lameness detection for horses. Emphasis is on classification performance, but we also discuss estimation of the unknown parameter function.     

An Improved Estimation in Regression Parameter Matrix in Multivariate Regression Model

We consider shrinkage and preliminary test estimation strategies for the matrix of regression parameters in multivariate multiple regression model in the presence of a natural linear constraint. We suggest a shrinkage and preliminary test estimation strategies for the parameter matrix. The goal of this article is to critically examine the relative performances of these estimators in the direction of the subspace and candidate subspace restricted type estimators. Our analytical and numerical results show that the proposed shrinkage and preliminary test estimators perform better than the benchmark estimator under candidate subspace and beyond. The methods are also applied on a real data set for illustrative purposes.     

Quotas as a ‘fast track’ to equal representation for women

Recent years have witnessed the rapid diffusion of electoral gender quotas. Today, about fourty countries around the world have introduced gender quotas for parliamentary elections, either by constitutional amendment or electoral law. Also, quotas for public election have been laid down in major political parties' statutes in more than fifty countries. This article, which is based on the first worldwide overview of the use of quotas, presents general trends in quota adoption. It identifies two discourses: the incremental track versus the fast track to women's parliamentary representation, and argues that the Scandinavian countries – which represent the incremental track – may no longer be a valid model for ways to improve women's representation. The article also analyses the implementation process, and concludes that, without specifications of quota provisions that match the electoral system in question, and rules about the rank order of candidates as well as sanctions for non-compliance, quota provisions may be merely symbolic.     

需求预测综合模型及其与库存决策的集成研究

准确的预测有助于企业做出有效的决策,包括生产计划、定价和促销决策等,以减少库存、提高客户满意度和企业竞争力． 以快速消费品为研究对象,分析其需求影响因素,建立了时间序列分析与多元回归整合的需求预测综合模型; 将此预测模型引入到库存决策中,构建了基于库存成本最小的需求预测与库存决策集成模型,并借助变邻域搜索算法获得模型的参数值; 最后,选用实际数据,验证了所构建的需求预测综合模型、物流需求预测与库存决策集成模型及其求解方法的有效性．     

流密码与纠错码联合设计新方向——快速相关攻击译码算法研究进展

快速相关攻击是分析流密码组合生成器最有效的攻击方法,其核心思想是将组合流密码的破译转化为译码问题,利用纠错码的译码技术实现对组合流密码的攻击。近年来,基于纠错码译码技术的流密码快速相关攻击技术有重要的进展和应用,因此总结快速相关攻击技术的发展现状,提出并分析新的快速相关攻击问题,有重要的学术和应用价值。该文首次将流密码快速相关攻击模型应用到纠错码理论中,提出流密码和纠错码联合设计新的研究方向,其研究成果有望解决极低信噪比环境下的可靠通信这一当前通信领域的难题。依据快速相关攻击的基本原理,分析并比较了4类典型快速相关攻击算法,即Meier-Staffelbach型算法,分别基于卷积码和Turbo码的攻击算法,CJS算法和基于LDPC码的快速相关攻击算法。最后得出快速相关攻击算法的一般适用准则,指出了快速相关攻击中尚未解决的问题和进一步的研究内容。     

不同小波基下的SAR图像相干斑抑制性能分析

分析了合成孔径雷达(SAR)图像中相干斑(Speckle)的统计特性。基于小波变换(WT),给出了一种多尺度的Speckle滤波方法,该方法利用小波分解后的HH分量(低频分量)作Speckle抑制,得到图像Speckle的相当大的改进。文中还对不同的小波基进行了理论分析,并利用实验数据,从等效视数(ENL)角度进行了比较,证实了不同小波基下该方法对Speckle具有较好的抑制性能。     

子波变换在超宽带雷达中的应用

介绍了连续子波变换的定义和性质：基于子波变换在时域和频域的良好局部化性质;研究其在宽带信号的相关处理和超宽带雷达目标特征提取两个方面的应用,理论分析和试验结果表明,在超宽带雷达信号处理中,宽带相关处理相当于时间尺度域的匹配滤波,采用连续子波变换从频域采样数据中提取目标散射中心是一种较为鲁棒的方法。     

R&D项目中止决策的小波网络模式识别

如何科学、合理、有效地识别和中止那些行将失败或没有前途的 R&D项目 ,对于企业成功地进行技术创新、建立和保持竞争优势 ,显然具有十分重要的理论价值和实践意义 .本文根据中止决策的实质及模式识别的基本原理 ,探讨了定量地中止 R&D项目的一般模式识别程序 .同时 ,基于小波基具有很强的自适应数据和函数变化的能力 ,进一步研究给出了一种基于小波网络的 R&D项目中止决策分析模式识别新方法 ,并在最小均方能量准则下 ,采用相应的共轭梯度学习算法求解子波函数线性组合的尺度和时延参数 ,以及神经网络的权值 .本研究不仅克服了现有 R&D项目中止决策分析技术在构造与求解模型方面的某些局限性 ,并且为R&D项目的跟踪管理提供了一种新的理论依据 ,为具体决策单位和政府有关部门对正在实施的 R&D项目作中止决策提供了一种可操作的决策方法和程序