Point and Interval Estimation, From One-Order Statistic, of the Location Parameter of an Extreme-Value Distribution with Known Scale Parameter and of the Scale Parameter of a Weibull Distribution with Known Shape Parameter

This paper derives a one-order statistic estimator ?mn b for the location parameter of the (first) extreme-value distribution of smallest values with cumulative distribution function F(x;u,b) = 1 - exp {-exp[(x-u)/b]} using the minimum-variance unbiased one-order statistic estimator for the scale parameter of an exponential distribution, as was done in an earlier paper for the scale parameter of a Weibull distribution. It is shown that exact confidence bounds, based on one-order statistic, can be easily derived for the location parameter of the extreme-value distribution and for the scale parameter of the Weibull distribution, using exact confidence bounds for the scale parameter of the exponential distribution. The estimator for u is shown to be b ln cmn + xmn, where xmn is the mth order statistic from an ordered sample of size n from the extreme-value distribution with scale parameter b and Cmn is the coefficient for a one-order statistic estimator of the scale parameter of an exponential distribution. Values of the factor cmn, which have previously viously been tabulated for n = 1(1)20, are given for n = 21(1)40. The ratios of the mean-square-errors of the maximum-likelihood estimators based on m order statistics to those of the one-order statistic estimators for the location parameter of the extreme-value distribution and the scale parameter of the Weibull distribution are investigated by Monte Carlo methods. The use of the table and related tables is discussed and illustrated by numerical examples.     

On Shrinkage Estimation of the Exponential Scale Parameter

This paper shows some simple shrunken estimators for the scale parameter of an exponential distribution and compares them with minimum MSE estimator and the estimator proposed by Pandey We have also obtained a Bayes estimator, which is a shrinkage estimator and has smaller MSE than the estimator (sample mean) n/(n + 1) if sample size, n, is small and other restrictions apply.     

基于极大值截尾和偏斜分类的Alpha-stable分布参数估计新方法

在分析Alpha-stable分布概率密度函数特性的基础上,充分利用Alpha-stable分布拖尾的渐进Pareto特性和不对称性,本文提出了一种新的Alpha-stable分布极大值估计方法.仿真和分析表明该方法提高了参数估计的准确性和稳定性.     

Sinusoidal Polynomial Parameter Estimation Using the Distribution Derivative

In this paper, we present a method to estimate the parameters of a generalized sinusoidal model. A generalized sinusoid $x$ is defined as a polynomial in the log domain, with complex coefficients $alpha_{i}$ : $x(t)=exp(sum_{i} alpha_{i} t^i)$, where $i=0cdots Q$. The method is based on the distribution derivative of the signal and operates in the transform domain. The method is very general and can use any linear transform such as the Fourier transform or the wavelet transform, or even combinations of linear transforms. Examples with the Fourier transform are given. The Fourier-based estimation methods are evaluated using synthetic signals and have performance very close to the theoretical bound.      

Bayes Interval Estimation for the Shape Parameter of the Power Distribution

The power distribution is considered as failure model and uses a square-error loss function. Bayes credibility interval estimators for the shape parameter have been obtained assuming 1) the following priors for the shape parameter: Jeffrey's invariant prior, gamma, and inverted gamma; 2) the following priors for reliability: beta and log gamma function. It is straightforward to obtain estimators for reliability when the estimators for the shape parameter are known.     

Small-Sample Estimation for the Lognormal Distribution With Unit Shape Parameter

Let X be a random variable such that In [(X - ?)/?] has a s-normal distribution with mean zero and variance one. Then X has a 3-parameter lognormal distribution with the third parameter, the shape parameter, fixed at unity. This paper presents the coefficients required to construct the best linear unbiased estimators (BLUEs) of ? and ? for samples of size fifteen and less. The variances and covariances of these estimators are provided. These estimators yield the BLUEs of the mean, standard deviation, and percentiles of X since these quantities are linear functions of ? and ?. Blom's estimators and maximum likelihood estimators compare favorably with the BLUEs.     

G分布族参数估计新方法

提出了G分布族参数估计的新方法,首先详细分析了当前普遍采用的基于矩估计的G分布族参数估计方法存在的理论缺陷,在此基础上,提出了一种基于Mellin变换的G分布族统一的参数估计方法。该方法以Mellin变换为出发点,详细推导了G分布族中各分布对应的第一个、第二个第二类型的特征函数和它们各自对应的对数矩和对数累积量,最终获得了各分布参数估计简洁的迭代表达式。文中所提方法不但克服了各分布的矩估计器面临的诸多不足,更重要的是把视数同其他参数一样视为待估计的参数,且能够快速、准确地迭代出它们的估计值,保证了G分布族中各分布的拟合精度。以KL(Kullback-Leibler)度量、MSE(Mean Square Error)度量和K-S(Kolmogorov-Smirnov)检验为定量评估准则,对不同分辨率、不同视数的实测SAR图像分别采用文中所提各分布估计器与对应的矩估计器进行拟合实验,实验结果的全面对比分析证明了所提方法的有效性。     

Simultaneous Estimation of Location and Scale Parameters of the Weibull Distribution

This paper deals with the simultaneous estimation of the location parameter ? and the scale parameter ? of the Weibull distribution when both are unknown and the shape parameter ? is known. The best linear unbiased estimate (BLUE) (?, ?) based on a subset of k optimum ordered observations selected from the whole sample is compared with 1) Ogawa's asymptotically best linear estimate (ABLE) (?*, ?*) based on k ordered observations whose ranks are approximated by an asymptotic optimum selection, and 2) the BLUE based on the ranks in 1). Tables facilitating the computation of (?, ?) based on k = 3, 4 optimum ordered observations are provided.     

基于零阶统计量的Alpha稳定分布参数估计方法

随着无线电技术的迅速发展,实际应用中遇到了很多非高斯信号或者尖脉冲噪声信号.此类过程具有显著的脉冲特性,其统计特性严重偏离高斯分布,Alpha稳定分布为这类过程提供了非常有用的理论工具.重点研究了Alpha稳定分布概率密度函数的计算以及参数估计方法.首先,介绍了Alpha稳定分布的基本理论,通过MATLAB仿真分析了四个参数对其分布的影响.然后,基于无穷级数逼近的方法实现了Alpha稳定分布概率密度函数的计算.最后,提出了对数矩和几何功率的概念,基于零阶统计量(ZOS)实现了Alpha稳定分布的参数估计,此方法具有更好的韧性和更高的精度.     

基于神经网络的多维非线性模型参数估计

本文详细研究了多层ＢＰ神经网络的特性，并提出了基于预畸变处理的广义线性模型参数估计方法。借助于函数组合器，提出了基于神经网络的多维非线性模型参数估计算法，从而为多维非线性参数辨识和分析提供一种有效途径。模拟结果表明，本文所提出的多维非线性模型参数估计方法是成功的。     

Life Tests with Periodic Change in the Scale Parameter of a Weibull Distribution

Two life testing procedures, namely, the progressively censored samples and Bartholomew's experiment are discussed under the assumption that the life of an item follows a specialized Weibull distribution. The scale parameter is different under two different conditions of usage of the item at regular intervals of time, the shape parameter remains unchanged throughout the experiment. The maximum likelihood estimates of the two scale parameters have been derived along with their variances. A numerical example illustrates the type of data and relevant calculations for the experiment involving progressively censored samples.     

A Bayesian Approach to Parameter and Reliability Estimation in the Poisson Distribution

For life testing procedures, a Bayesian analysis is developed with respect to a random intensity parameter in the Poisson distribution. Bayes estimators are derived for the Poisson parameter and the reliability function based on uniform and gamma prior distributions of that parameter. A Monte Carlo procedure is implemented to make possible an empirical mean-squared error comparison between Bayes and existing minimum variance unbiased, as well as maximum likelihood, estimators. As expected, the Bayes estimators have mean-squared errors that are appreciably smaller than those of the other two.     

Parameter Estimation for Locally Linear FM Signals Using a Time-Frequency Hough Transform

An estimator for the phase parameters of mono- and multicomponent FM signals, with both good numerical properties and statistical performance is proposed. The proposed approach is based on the Hough transform of the pseudo-Wigner-Ville time-frequency distribution (PWVD). It is shown that the numerical properties of the estimator can be improved by varying the PWVD window length. The effect of the window time extent on the statistical performance of the estimator is delineated. Experimental data is used for validation of the statistical properties.      

基于EM算法的GO分布参数最大似然估计

G0分布是目前合成孔径雷达(Synthetic Aperture Radar,SAR)图像数据建模的一个重要模型,建模能力强、实用性好,受到了广泛的关注.G0分布的应用离不开准确有效的参数估计,而由于G0分布表达式复杂,统计意义上最优的最大似然估计法一直没能用在G0分布上.本文首先给出了一种新的方式来推导得出G0分布,在此基础上,采用最大期望(Expectation Maximization,EM)算法为G0分布给出一种有效的最大似然参数估计方法.文中的方法与现有的G0分布参数估计方法通过实验进行了比较,实验结果充分证明了所提方法的有效性.     

A Statistical Framework for Estimation of Full-Chip Leakage-Power Distribution Under Parameter Variations

This paper presents a novel framework for accurate estimation of key statistical parameters of the subthreshold-and gate-leakage distributions of a chip under parameter variations while considering both within-die and die-to-die variabilities in process (P), temperature (T), and supply voltage (V). For the first time, temperature variations and, more importantly, electrothermal couplings between junction (substrate or die) temperature and leakage power have been accounted for in a full-chip leakage estimation methodology. In the proposed framework, instead of exact leakage distribution profile, its statistically important parameters, such as nominal value and spread, are computed. Initially, at the transistor level, a quantitative analysis of the relative sensitivities of device leakage components to P-T-V variations is performed to extract a transistor-level variation model. It is shown that the proposed statistical model, as compared to others in the literature, shows better agreement with BSIM¹ model-based simulations. It is also demonstrated that failing to account for temperature variations and electrothermal couplings can result in significant inaccuracy in chip-level leakage estimation. Furthermore, the full-chip leakage-power distribution is used to estimate the leakage-constrained yield under the impact of variations. The calculations show that yield is significantly lowered due to the within-die and die-to-die process and temperature variations. Subsequently, the proposed framework is applied in the leakage estimation of complex logic circuits with a consideration of spatial correlations of process parameters and transistor stacking effects.     

Linear Estimation of the Parameters of the Extreme-Value Distribution Based on Suitably Chosen Order Statistics

Best linear unbiased estimates (BLUEs) based on a few order statistics are found for the location and scale parameters of the extreme-value distribution (Type-I asymptotic distribution of smallest values), when one or both parameters are unknown, such that the estimates have maximum efficiencies among the BLUEs based on the same number of order statistics. These estimates are then compared with the BLUEs and asymptotically best linear estimates (ABLEs) based on a few order statistics whose ranks were determined from the spacings that maximize the asymptotic efficiencies of the ABLEs. An application to the Weibull distribution is given.     

RS码参数的盲估计

提出了一种基于对偶码的RS码盲识别方法.建立接收序列的矩阵模型,通过统计该矩阵模型对应的低重量向量的数量,估计RS码的码长和分组起点.通过计算对偶码,估计校验矩阵、生成多项式、本原多项式等参数.实验表明在较高的误码率条件下也能达到较好的识别效果.     

点火回路电感的计算机辅助测量

点火回路中各部分的电感都为nH量级,并且其体积往往很小,要准确的测量其各部分电感的大小具有很大的难度。本文介绍了一种利用计算机辅助分析回路电感的方法,通过建立计算机仿真模型,利用放电时回路中各个参考点之间的电压波形的差异,通过改变测试参考点,比较实测电压波形与仿真电压波形来得到点火回路中电感的分布情况。     

Parameter Estimation in the Stenosed Coronary Circulatory System

In a stenosed coronary circulation, the following parameters were estimated from pressure measurements: coronary vascular resistances, transmural blood flow, and endocardial to epicardial (ENDO: EPI) flow ratio. This was done by modeling the hemodynamics of the coronary system with an analogous electrical circuit. The circuit topology consists of a nonlinear stenosis resistance and an endocardial branch in parallel with an epicardial branch. The unknown, linear endocardial and epicardial resistances were determined by a least-squares estimation method with three inputs: the left ventricular pressure (LVP), the aortic pressure (AP), and the distal coronary arterial pressure (DP). In this paper, computer simulations also show that the input DP may be replaced by a phasic arterial coronary flow synthesized from its mean systolic and diastolic values. In animal studies, DP may be measured directly while such pressure measurements are difficult to obtain clinically since it requires passing a catheter distal to a stenosis. However, mean systolic and diastolic coronary arterial flow have been obtained in humans via video densitometry. Thus, this method may potentially be useful in clinical situations. Validity of the method was tested with 23 animal studies using swine under a variety of physiological conditions. Linear regression analyses were made between the ENDO: EPI flow ratios estimated by the model and those measured by use of the radiolabeled microsphere technique.     

基于微多普勒的微动点目标参数估计

给出了基于微多普勒的点目标参数估计一般方法,以时频分析和变采样率滤波为工具,系统地研究了点目标微多普勒提取,给出了几种典型的微动点目标参数估计方法,包括二阶运动、三阶运动、阻尼运动、正弦振动和复合运动,仿真结果证明了该方法的有效性。