One-Step Estimator Paths for Concave Regularization

The statistics literature of the past 15 years has established many favorable properties for sparse diminishing-bias regularization: techniques that can roughly be understood as providing estimation under penalty functions spanning the range of concavity between ?₀ and ?₁ norms. However, lasso ?₁-regularized estimation remains the standard tool for industrial Big Data applications because of its minimal computational cost and the presence of easy-to-apply rules for penalty selection. In response, this article proposes a simple new algorithm framework that requires no more computation than a lasso path: the path of one-step estimators (POSE) does ?₁ penalized regression estimation on a grid of decreasing penalties, but adapts coefficient-specific weights to decrease as a function of the coefficient estimated in the previous path step. This provides sparse diminishing-bias regularization at no extra cost over the fastest lasso algorithms. Moreover, our gamma lasso implementation of POSE is accompanied by a reliable heuristic for the fit degrees of freedom, so that standard information criteria can be applied in penalty selection. We also provide novel results on the distance between weighted-?₁ and ?₀ penalized predictors; this allows us to build intuition about POSE and other diminishing-bias regularization schemes. The methods and results are illustrated in extensive simulations and in application of logistic regression to evaluating the performance of hockey players. Supplementary materials for this article are available online.     

Regularized Principal Component Analysis for Spatial Data

In many atmospheric and earth sciences, it is of interest to identify dominant spatial patterns of variation based on data observed at p locations and n time points with the possibility that p > n. While principal component analysis (PCA) is commonly applied to find the dominant patterns, the eigenimages produced from PCA may exhibit patterns that are too noisy to be physically meaningful when p is large relative to n. To obtain more precise estimates of eigenimages, we propose a regularization approach incorporating smoothness and sparseness of eigenimages, while accounting for their orthogonality. Our method allows data taken at irregularly spaced or sparse locations. In addition, the resulting optimization problem can be solved using the alternating direction method of multipliers, which is easy to implement, and applicable to a large spatial dataset. Furthermore, the estimated eigenfunctions provide a natural basis for representing the underlying spatial process in a spatial random-effects model, from which spatial covariance function estimation and spatial prediction can be efficiently performed using a regularized fixed-rank kriging method. Finally, the effectiveness of the proposed method is demonstrated by several numerical examples.     

A Cross-Entropy Approach to the Estimation of Generalized Linear Multilevel Models

In this article, we use the cross-entropy method for noisy optimization for fitting generalized linear multilevel models through maximum likelihood. We propose specifications of the instrumental distributions for positive and bounded parameters that improve the computational performance. We also introduce a new stopping criterion, which has the advantage of being problem-independent. In a second step we find, by means of extensive Monte Carlo experiments, the most suitable values of the input parameters of the algorithm. Finally, we compare the method to the benchmark estimation technique based on numerical integration. The cross-entropy approach turns out to be preferable from both the statistical and the computational point of view. In the last part of the article, the method is used to model the probability of firm exits in the healthcare industry in Italy. Supplemental materials are available online.     

过渡态理论在化学体系中的2个应用实例*

在大学本科一二年级的物理化学教学中,化学动力学章节的学习涉及到对不同时间尺度的认识。本文利用过渡态理论得到的速率常数表达公式,以乙烷分子碳/碳单键旋转和水分子氢键交换的动力学过程为例,对这2个应用实例的动力学过程所发生的时间尺度做出估算。这些时间尺度的估算对于学生理解不同动力学过程的物理图像至关重要,同时也有助于加强学生对重要公式的理解并能够将这些公式在化学体系中灵活运用。     

Fabrication and Characterization of Zinc Oxide Nanowire Based Two-electrode Capacitive Biosensors on Flexible Substrates for Estimating Glucose Content in a Sample

The present work deals with fabrication and characterization of the zinc oxide (ZnO) nanowire based novel two-electrode capacitive biosensors on flexible Polyethylene terephthalate (PET) substrates for accurate estimation of glucose by analyzing the fundamental dielectric nature of the relevant sample. The morphology and crystalline quality of grown nanowires are analyzed by using field-emission scanning electron microscope (FESEM) and X-ray diffractometer (XRD), respectively. Current and capacitance values of the device have been studied for ten different glucose concentrations relevant to the physiological standards. The analytical performance of the devices in terms of enzyme activity, reliability and flexibility has also been evaluated.     

异质传感器数据的最优线性融合

研究了异质传感器对参数矢量进行的测量，测量数据基于线性均方估计的最优融合算法。提出和证明了异质传感器数据的最优线性融合定理，并得出“精度再差的传感器参与数据融合后都有利于提高系统的测量精度”这一结论。     

具有外观测误差的INS传递对准的性能分析

对于给定的传递对准的误差模型，针对常值外观测速度、位置误差和随机外观测速、位置度误差两种情况，分别对惯导系统的传递对准误差模型的解算偏差和Kalman滤波器的估计偏差进行了仿真分析。结果表明，外观测误差引起的INS传递对准的估计偏差不能忽视，为采用更优的估计方法提供了依据。     

一种用于分散化滤波的融合算法及其在组合导航系统中的应用

提出了一种局部估计相关时的最优融合算法，其特点是加权矩阵是对角阵，即全局估计中的各分量分别是局部估计中相应分量的线性组合，在此融合算法中局部估计是最优的，并且不存在由主滤波器向子滤波器的信息反馈，因此具有较好的客措性。给出了最优加权矩阵的存在性和唯一性的证明，并通过与现有类似融合算法的对比，表明该算法具有计算量小的优点。最后，以组合导航系统为例作了仿真计算，结果表明该融合算法是可行的。     

Efficient error estimation in quantum key distribution

In a quantum key distribution(QKD)system,the error rate needs to be estimated for determining the joint probability distribution between legitimate parties,and for improving the performance of key reconciliation.We propose an efficient error estimation scheme for QKD,which is called parity comparison method(PCM).In the proposed method,the parity of a group of sifted keys is practically analysed to estimate the quantum bit error rate instead of using the traditional key sampling.From the simulation results,the proposed method evidently improves the accuracy and decreases revealed information in most realistic application situations.     

Adaptive RAKE receiver in chaos based pilot-added DS-CDMA system

This paper presents the theoretical analysis of adaptive multiuser RAKE receiver scheme in frequency selective fading channel for direct-sequence code division multiple access (DS-CDMA) system. Least mean square (LMS) algorithm is used to estimate the channel coefficients. Chaotic sequences are used as spreading sequence and corresponding bit error rate (BER) in closed form is derived for imperfect channel estimation conditions. Performances of chaotic sequences are compared with pseudorandom noise (PN) sequences. Under perfect synchronization assumption, various simulation results are shown to investigate the performance of the proposed system.