Resampling estimates of precision in emission tomography

Methods for estimating the regional variance in emission tomography images which arise from the Poisson nature of the raw data are discussed. The methods are based on the bootstrap and jackknife methods of statistical resampling theory. The bootstrap is implemented in time-of-flight PET (positron emission tomography); the same techniques can be applied to non-time-of-flight PET and SPECT (single-photon-emission computed tomography). The estimates are validated by comparing them to those obtained by repetition of emission scans, using data from a time-of-flight positron emission tomograph. Simple expressions for the accuracy of the estimates are given. The present approach is computationally feasible and can be applied to any reconstruction technique as long as the data are acquired in a raw, uncorrected form.     

Quadrature receiver mismatch calibration

This article introduces nonlinear regression techniques to estimate gain and phase mismatches between the in-phase (I) and quadrature (Q) branches of a quadrature receiver. Under modest assumptions, the system intrinsically follows a nonlinear regression model. The algorithm is effective, easily implemented, customizable, and requires few assumptions. Large-sample, jackknife, and bootstrap techniques provide on-line error assessment and parameter inference     

A note on the validity of statistical bootstrapping for estimating the uncertainty of tensor parameters in diffusion tensor images

Diffusion tensors are estimated from magnetic resonance images (MRIs) that are diffusion-weighted, and those images inherently contain noise. Therefore, noise in the diffusion-weighted images produces uncertainty in estimation of the tensors and their derived parameters, which include eigenvalues, eigenvectors, and the trajectories of fiber pathways that are reconstructed from those eigenvalues and eigenvectors. Although repetition and wild bootstrap methods have been widely used to quantify the uncertainty of diffusion tensors and their derived parameters, we currently lack theoretical derivations that would validate the use of these two bootstrap methods for the estimation of statistical parameters of tensors in the presence of noise. The aim of this paper is to examine theoretically and numerically the repetition and wild bootstrap methods for approximating uncertainty in estimation of diffusion tensor parameters under two different schemes for acquiring diffusion weighted images. Whether these bootstrap methods can be used to quantify uncertainty in some diffusion tensor parameters, such as fractional anisotropy (FA), depends critically on the morphology of the diffusion tensor that is being estimated. The wild and repetition bootstrap methods in particular cannot quantify uncertainty in the principal direction (PD) of isotropic (or oblate) tensor. We also examine the use of bootstrap methods in estimating tensors in a voxel containing multiple tensors, demonstrating their limitations when quantifying the uncertainty of tensor parameters in those locations. Simulation studies are also used to understand more thoroughly our theoretical results. Our findings raise serious concerns about the use of bootstrap methods to quantify the uncertainty of fiber pathways when those pathways pass through voxels that contain either isotropic tensors, oblate tensors, or multiple tensors.      

The bootstrap and its application in signal processing

The bootstrap is an attractive tool for assessing the accuracy of estimators and testing hypothesis for parameters where conventional techniques are not valid, such as in small data-sample situations. We highlight the motivations for using the bootstrap in typical signal processing applications and give several practical examples. Bootstrap methods for testing statistical hypotheses are described and we provide an analysis of the accuracy of bootstrap tests. We also discuss how the bootstrap can be used to estimate a variance-stabilizing transformation to define a pivotal statistic, and we demonstrate the use of the bootstrap for constructing confidence intervals for flight parameters in a passive acoustic emission problem     

Robust parameter-estimation using the bootstrap method for the2-parameter Weibull distribution

This paper proposes bootstrap robust estimation methods for the Weibull parameters; it applies bootstrap estimators of order statistics to the parametric estimation procedure. Estimates of the Weibull parameters are equivalent to the estimates using the extreme value distribution. Therefore, the bootstrap estimators of order statistics for the parameters of the extreme value distribution are examined. Accuracy and robustness for outliers are examined by Monte Carlo experiments which indicate adequate efficiency of the proposed reliability estimators for data with some outliers     

Bootstrap multiple tests applied to sensor location

A method for finding optimal locations of vibration sensors within a group of sensors for detecting knock in combustion engines is proposed. It differs from other techniques in that only signal processing and statistical tests are used. The method is based on linearly predicting the in-cylinder pressure in a combustion chamber from the output signals of a group of vibration sensors being distributed on the engine wall. The irrelevancy of a sensor in the group is characterized by the closeness to zero of a “coherence gain” explained by this sensor at some frequencies of interest. We formulate multiple hypotheses, define suitable statistics, approximate their sampling distributions by the nonparametric bootstrap method, and construct the generalized sequentially rejective Bonferroni multiple test. The level of accuracy of bootstrap tests is superior to that of asymptotic tests, provided the test statistic used is asymptotically pivotal. To achieve pivoting, we transform the test statistic into a variance stable scale where we use a bootstrap technique to approximate the variance stabilizing transformation. Simulations results as well as results of an experiment performed on a test bed with a four-cylinder engine emphasize the applicability of the method     

基于PowerBuilder的酒店房租自动统计功能设计

PowerBuilder作为专业的数据库开发工具,一直深受国内广大用户的喜爱。在酒店客房的房租管理中,不同的出租方式有不同计算方法;不同的进店时间有不同的统计方法,如何根据这些不同的管理规定,利用PowerBuilder实现酒店房租的自动统计功能是讨论的主要内容。     

Dynamically optimized spatiotemporal prioritization for spectrum sensing in cooperative cognitive radio

In this paper, an enhanced cooperative, statistics-driven spectrum sensing algorithm, called Dynamically Optimized Spatiotemporal Prioritization (DOSP), is developed for improving spectrum sensing efficiency in the media access control (MAC) layer of cognitive radio (CR) systems. The target of the DOSP algorithm is to improve spectrum sensing efficiency and achieve better spectrum access opportunities by prioritizing channels for fine sensing. The sensing priority is determined dynamically and intelligently based on an optimal statistical fusion that jointly considers both the local statistics obtained by the individual cognitive radios as well as the long-term spatiotemporal statistics obtained by other cognitive radios in the network. As such, the individual cognitive radio peers work together to get the most out of available spectrum opportunities. Numerical results demonstrate that the proposed DOSP algorithm is capable of achieving better performance compared with recently reported cooperative spectrum sensing methods in terms of overhead and percentage of missed spectrum opportunities. Furthermore, results show that the DOSP algorithm is more robust to the environment of low cognitive radio densities than that by using other state-of-the-art cooperative spectrum sensing methods.     

一种新的空间数据不确定性重建方法

在重建空间数据时,如果条件数据较少甚至没有任何条件数据,重建结果常常出现较多的不确定性,此时适合采用基于统计原理的随机模拟方法重建空间数据.多点信息统计法（Multiple-Point Statistics,MPS）是目前随机模拟的主流方法,它可以将训练图像中提取的本质特征复制到重建区域.由于传统采用线性降维的MPS无法较好处理非线性数据,而局部线性嵌入（Locally Linear Embedding,LLE）可以实现对非线性数据的降维,因此提出LLE与MPS相结合的空间数据不确定性重建方法.利用该方法对图像数据进行重建实验,实验结果证明该方法的有效性.     

基于短信的统计系统研究

在分析短信统计具有及时性、突破时空限制、成本低、交互性强等特点的基础上，构建了短信统计系统的模型，说明了利用短信统计系统进行统计调查与信息发布的方式，提出短信在统计中应用时应注意安全性、信息传递方式、代码化与标准化、通信网络拥堵与信息丢失等问题，并给出了相应的解决办法。     

基于自助法抽样分布的ROC分析

对于两类分类问题,错误率描述了分类器在单一判决阈值下的推广性能.然而,当两类错分代价不相等时,人们往往需要了解分类器在不同判决阈值下的性能.ROC(receiver operating characteristics)分析提供了一种全面了解分类器性能的途径,但是由于实际样本个数有限,训练集和测试集的划分必然会影响ROC曲线的形状,从而导致ROC分析具有不确定性.针对Fisher线性判别,本文提出了一种基于自助法抽样分布的ROC曲线生成方法.具体如下:定义Fisher线性分类器对测试样本的判别值为统计量,由自助法分别得到Fisher线性分类器对两类测试样本判别值的两个抽样分布,在这两个抽样分布范围内连续地改变阈值即可得到ROC曲线.实验表明,与随机划分和10重交叉验证相比,由本文方法生成的ROC曲线形状更加稳定,并且相应ROC曲线下面积的置信区间更短,也即本文方法能产生更加精确的ROC分析.     

BOOTSTRAP TECHNIQUE FOR ROC ANALYSIS： A STABLE EVALUATION OF FISHER CLASSIFIER PERFORMANCE

This paper presents a novel bootstrap based method for Receiver Operating Characteristic (ROC) analysis of Fisher classifier. By defining Fisher classifier’s output as a statistic, the bootstrap technique is used to obtain the sampling distributions of the outputs for the positive class and the negative class respectively. As a result, the ROC curve is a plot of all the (False Positive Rate (FPR), True Positive Rate (TPR)) pairs by varying the decision threshold over the whole range of the boot- strap sampling distributions. The advantage of this method is, the bootstrap based ROC curves are much stable than those of the holdout or cross-validation, indicating a more stable ROC analysis of Fisher classifier. Experiments on five data sets publicly available demonstrate the effectiveness of the proposed method.     

Low-Voltage Bootstrapped CMOS Drivers With Efficient Conditional Bootstrapping

In this paper, a set of low-voltage bootstrapped CMOS drivers are presented to reduce power consumption and improve switching speed for driving a large capacitive load. The proposed drivers can reduce the power consumption by making bootstrap operations conditional to input statistics. They also improve switching speed by providing larger bootstrap voltages for the same amount of integrated bootstrap capacitance as compared with conventional bootstrapped drivers. The proposed drivers were designed using 0.18- CMOS technology. The comparison results indicate that the proposed drivers achieve power savings up to 97% with 13%-22% improvements on switching speed as compared with the conventional design.     

Bootstrap Methods and Applications

Given the wealth of literature on the topic supported by solutions to practical problems, we would expect the bootstrap to be an off-the-shelf tool for signal processing problems as are maximum likelihood and least-squares methods. This is not the case, and we wonder why a signal processing practitioner would not resort to the bootstrap for inferential problems. We may attribute the situation to some confusion when the engineer attempts to discover the bootstrap paradigm in an overwhelming body of statistical literature. Our aim is to give a short tutorial of bootstrap methods supported by real-life applications. This pragmatic approach is to serve as a practical guide rather than a comprehensive treatment, which can be found elsewhere. However, for the bootstrap to be successful, we need to identify which resampling scheme is most appropriate.     

Nonsupervised ranking of different segmentation approaches: application to the estimation of the left ventricular ejection fraction from cardiac cine MRI sequences

A statistical methodology is proposed to rank several estimation methods of a relevant clinical parameter when no gold standard is available. Based on a regression without truth method, the proposed approach was applied to rank eight methods without using any a priori information regarding the reliability of each method and its degree of automation. It was only based on a prior concerning the statistical distribution of the parameter of interest in the database. The ranking of the methods relies on figures of merit derived from the regression and computed using a bootstrap process. The methodology was applied to the estimation of the left ventricular ejection fraction derived from cardiac magnetic resonance images segmented using eight approaches with different degrees of automation: three segmentations were entirely manually performed and the others were variously automated. The ranking of methods was consistent with the expected performance of the estimation methods: the most accurate estimates of the ejection fraction were obtained using manual segmentations. The robustness of the ranking was demonstrated when at least three methods were compared. These results suggest that the proposed statistical approach might be helpful to assess the performance of estimation methods on clinical data for which no gold standard is available.     

An adaptation procedure on envelope statistics for predistorter designs based on statistical modeling methods

There have long been some experiment findings that the behavior of a power amplifier (PA) to digitally modulated stimuli depends on its input statistics. Correspondingly, there have been efforts in characterizing PA behaviors and, more recently, in designing amplifier linearization devices by including the information of envelope statistics or using statistical methods. Superior performance have been reported for resultant linearizer designs compared with similar designs based on conventional approaches. In this brief, an adaptation procedure on envelope statistics will be introduced that can further improve the performance of any predistorter design derived from a statistical amplifier model, or more generally, derived from the sample data sets of the input and the output of the PA.     

Robust estimation of bioaffinity assay fluorescence signals.

In this paper, the challenging problem of robust mean-signal estimation of a single-step microparticle bioaffinity assay is investigated. For this purpose, a density estimation-based robust algorithm (DER) was developed. The DER algorithm was comparatively evaluated with four other parameter estimation methods (mean value, median filtering, least square estimation, Welsch robust m-estimator). Two important questions were raised and investigated: 1) Which of the five methods can robustly estimate the mean bioaffinity signal? and 2) How many microparticles need to be measured in order to obtain an accurate estimate of the mean signal value? To answer the questions, bootstrap and coefficient of variation (CV) analyses were performed. In the CV analysis, the DER algorithm gave the best results: The CV ranged from 0.8% to 4.9% when the number of microparticles used for the mean signal estimation varied from 800 to 30. In the bootstrap analysis of the standard error, the DER algorithm had the smallest variance. As a conclusion, it can be underlined that: 1) of all methods tested, the DER algorithm gave the most consistent and reproducible results according to the bootstrap and CV analysis; 2) using the DER algorithm accurate estimates could be calculated based on 80-100 particles, corresponding to a typical assay measurement time of 1 min; and 3) the investigated bioaffinity signals contained a large number of outliers (observations that severely deviate from the majority of data) and therefore robust techniques were necessary for the mean signal estimation tasks.     

Nonparametric Model Comparison and Uncertainty Evaluation for Signal Strength Indoor Location

Indoor Location (IL) using Received Signal Strength (RSS) is receiving much attention, mainly due to its ease of use in deployed IEEE 802.11b (WiFi) wireless networks. Fingerprinting is the most widely used technique. It consists of estimating position by comparison of a set of RSS measurements, made by the mobile device, with a database of RSS measurements whose locations are known. However, the most convenient data structure to be used and the actual performance of the proposed fingerprinting algorithms are still controversial. In addition, the statistical distribution of indoor RSS is not easy to characterize. Therefore, we propose here the use of nonparametric statistical procedures for diagnosis of the fingerprinting model, specifically: 1) A nonparametric statistical test, based on paired bootstrap resampling, for comparison of different fingerprinting models and 2) new accuracy measurements (the uncertainty area and its bias) which take into account the complex nature of the fingerprinting output. The bootstrap comparison test and the accuracy measurements are used for RSS-IL in our WiFi network, showing relevant information relating to the different fingerprinting schemes that can be used.     

Time Series Modeling by a Second-Order Hammerstein System

In this paper, we discuss the modeling of an observed time series as the output of a second-order Hammerstein model driven by an independent and identically distributed (i.i.d.) Gaussian noise. The modeling is discussed from the point of view of the hypothesis testing (HT) of statistical indexes derived from higher order statistics (HOS). Three points of view can be adopted in HT. The more general approach is to not make any assumption on the index statistics (notably on the variance) and thus to use the T ² Hotelling test. The second point of view involves calculating a theoretical variance in order to define normalized variables whose statistics are known. The last approach is a mixed version of the others two. These three approaches are applied to statistical indexes derived from linear combination of slices of spectra from the third order up the fifth order. Some subcases need to use the derivatives of the polyspectra since general relationships are null and cannot be used for the HT. The second part of this paper is devoted to (linear and quadratic) kernel identification using the relationship defined for HT definition. As for the HT, several subcases have to be discussed using either the polyspectra or the derivatives of polyspectra.