Empirical Bayes Test for Two-parameter Exponential Distribution under Type-Ⅱ Censored Samples

The empirical Bayes test problem is considered for scale parameter of twoparameter exponential distribution under type-II censored data.By using wavelets estimation method,the EB test function is constructed,of which the asymptotic optimality and convergence rates are obtained.Finally,an example concerning the main result is given.

BAYESIAN PREDICTION FOR THE TWO-PARAMETER EXPONENTIAL DISTRIBUTION BASED ON TYPE Ⅱ DOUBLY CENSORING

The two-parameter exponential distribution is proposed to be an underlying model,and prediction bounds for future observations are obtained by using Bayesian approach.Prediction intervals are derived for unobserved lifetimes in one-sample prediction and two-sample prediction based on type Ⅱ doubly censored samples.A numerical example is given to illustrate the procedures,prediction intervals are investigated via Monte Carlo method,and the accuracy of prediction intervals is presented.

A Note on “Limit Distributions of Self-Normalized Sums” Using Cauchy-Generated Samples

In this case study, we would like to illustrate the utility of characteristic functions, using an example of a sample statistic defined for samples from Cauchy distribution. The derivation of the corresponding asymptotic probability density function is based on [1], elaborating and expanding the individual steps of their presentation, and including a small extension;our reason for such a plagiarism is to make the technique, its mathematical tools and ingenious arguments available to the widest possible audience.

Depth Estimation from a Single Image Based on Cauchy Distribution Model

Most approaches to estimate a scene’s 3D depth from a single image often model the point spread function (PSF) as a 2D Gaussian function. However, those methods are suffered from some noises, and difficult to get a high quality of depth recovery. We presented a simple yet effective approach to estimate exactly the amount of spatially varying defocus blur at edges, based on a Cauchy distribution model for the PSF. The raw image was re-blurred twice using two known Cauchy distribution kernels, and the defocus blur amount at edges could be derived from the gradient ratio between the two re-blurred images. By propagating the blur amount at edge locations to the entire image using the matting interpolation, a full depth map was then recovered. Experimental results on several real images demonstrated both feasibility and effectiveness of our method, being a non-Gaussian model for DSF, in providing a better estimation of the defocus map from a single un-calibrated defocused image. These results also showed that our method was robust to image noises, inaccurate edge location and interferences of neighboring edges. It could generate more accurate scene depth maps than the most of existing methods using a Gaussian based DSF model.

Prediction of nuclear charge density distribution with feedback neural network

Nuclear charge density distribution plays an important role in both nuclear and atomic physics,for which the two-parameter Fermi(2pF)model has been widely applied as one of the most frequently used models.Currently,the feedforward neural network has been employed to study the available 2pF model parameters for 86 nuclei,and the accuracy and precision of the parameter-learning effect are improved by introducing A^(1∕3)into the input parameter of the neural network.Furthermore,the average result of multiple predictions is more reliable than the best result of a single prediction and there is no significant difference between the average result of the density and parameter values for the average charge density distribution.In addition,the 2pF parameters of 284(near)stable nuclei are predicted in this study,which provides a reference for the experiment.

The Evolution of the Charge Density Distribution Function for Spherically Symmetric System with Zero Initial Conditions

The evolution of the charge density distribution function is simulated for both the case of a uniformly charged sphere with zero initial conditions and for the case of a non-uniform charged sphere. For the case of a uniformly charged sphere the comparison of a numerical result and an exact analytical demonstrated the agreement between the results. The process of “scattering” of a charged system under the influence of its own electric field has been illustrated on the basis of both the particle-in-cell method and the solution of the Cauchy problem for vector functions of the electric field and vector velocity field of a charged medium.

Interval Estimation in a Two Parameter Weibull Distribution Based on Type-2 Censored Data

In this paper, we consider the construction of the approximate profile- likelihood confidence intervals for parameters of the 2-parameter Weibull distribution based on small type-2 censored samples. In previous research works, the traditional Wald method has been used to construct approximate confidence intervals for the 2-parameter Weibull distribution under type-2 censoring scheme. However, the Wald technique is based on normality assumption and thus may not produce accurate interval estimates for small samples. The profile-likelihood and Wald confidence intervals are constructed for the shape and scale parameters of the 2-parameter Weibull distribution based on simulated and real type-2 censored data, and are hence compared using confidence length and coverage probability.

The Case of Nonzero Initial Conditions in the Evolution of the Charge Density Distribution Function for a Spherically Symmetric System

We explored the Cauchy problem for the evolution of the charge density distribution function for a spherically symmetric system with nonzero initial conditions. In our model, the evolution of the charge density distribution function is simulated for the case of a non-uniform charged sphere. The initial speed of the system is nonzero. The solution breaks down into two components: the first one describes the system’s motion as a whole and the second describes the process of the evolution of the charge density function under the influence of its own electric field in the center-of-mass system. In this paper we considered the characteristic features of the implementation of a difference scheme for numerical simulation. We also illustrate the process of “scattering” of a moving charged system under the influence of its own electric field on the basis of the solution of the Cauchy problem for vector functions of the electric field and vector velocity field of a charged medium.

Effective Truncation of a Student’s <i>t</i>-Distribution by Truncation of the Chi Distribution in a Chi-Normal Mixture

A Student’s t-distribution is obtained from a weighted average over the standard deviation of a normal distribution, σ, when 1/σ is distributed as chi. Left truncation at q of the chi distribution in the mixing integral leads to an effectively truncated Student’s t-distribution with tails that decay as exp (-q2t2). The effect of truncation of the chi distribution in a chi-normal mixture is investigated and expressions for the pdf, the variance, and the kurtosis of the t-like distribution that arises from the mixture of a left-truncated chi and a normal distribution are given for selected degrees of freedom 5. This work has value in pricing financial assets, in understanding the Student’s t--distribution, in statistical inference, and in analysis of data.

非对称Cauchy分布最大次序统计量的大数定律研究

基于非对称Cauchy分布随机阵列构造最大次序统计量,研究其大数定律问题,其中非对称Cauchy分布期望不存在。利用随机变量截尾技术和控制收敛定理,获得最大次序统计量的弱大数律和强大数律,并指出所获弱大数律不能推广到强大数律。另外,给出产生非对称Cauchy分布算法,并开展最大次序统计量的弱大数律和强大数律数据模拟工作。

基于排队论的某食品零售店收银系统优化研究

对于某食品零售店高峰期排队结账时间较长的问题,提出两种针对性的解决方案。通过选取节假日客流高峰时间段的数据进行现场采集,基于运筹学中的排队理论对该食品零售门店高峰期两小时的实际客流进行统计,运用python建立M/G/2模型,利用真实数据进行模拟仿真,根据仿真结果提出两种可行的优化方案,并对优化方案进行仿真计算。计算结果表明,不论是收银系统的改变还是排队模式的改变,都能够更加充分地利用环境资源、硬件资源和人力资源,能帮助该类门店缓解高峰时期客流拥堵的现象,由此见得,优化后的系统相较于优化前更加高效合理。这样门店就能够将更好的服务提供给消费者,从而提高消费者的满意程度,为企业带来更大的效益,门店可以根据实际情况安排合适的优化方式。

双截尾的Cauchy分布顺序统计量的渐近分布

设{Xk,1≤k≤n}独立同分布,X1:n,X2:n,…,Xn:n为其顺序统计量。当Xk服从参数为A和B(A<B)的双截尾的Cauchy分布时,得到其极端顺序统计量X1:n和Xn:n的渐近分布;当k(k>1)固定时,得到Xk:n和Xn-k+1:n的渐近分布;并且证明其极端顺序统计量X1:n和Xn:n是渐近独立的。

基于Cauchy稀疏分布的SAR图像超分辨算法

分析了SAR图像稀疏性的成因,利用具有稀疏特性的Cauchy分布作为SAR图像相位历史域数据的先验分布,建立了基于Cauchy稀疏分布的SAR图像超分辨模型,通过减少每次迭代中矩阵求逆的次数,给出了模型的快速迭代求解算法。利用SAR图像的稀疏性先验,将SAR图像超分辨问题转化为形式简单的约束极值问题,避免了基于lk范数正则化超分辨方法的模型定阶问题。最后,利用MSTAR实测数据,在相位历史域的实验结果证实了该方法有效的超分辨能力和快速求解能力。

基于Cauchy分布的红外视频运动目标检测

基于柯西分布统计模型和梯度信息,提出了一种用于热成像(红外)图像序列中行人目标检测的统计背景剔除方法.它不依赖传统红外行人检测方法的"热场"(hot-spot)假设(即认为人体区域总比周围环境热),而是用一种快速动态背景的重建方法建立初始化背景Gaussian模型,简化了Cauchy分布的参数估计问题.基于Gauchy分布的背景剔除被首先用来识别局部兴趣区域(Regions-Of-Interest,ROI).在每个兴趣区域将当前图像和背景图像的梯度信息组合成一个梯度图.对梯度图进行各向异性滤波和运用最大类间方差法进行自适应阈值处理后获得行人目标区域.经多组实验及与其他方法比较,结果表明提出的方法对热成像红外视频人体目标的位置和形状实现了较精确的检测,对环境、时间变化、极性翻转、"晕轮"效应等产生的影响,以及背景中杂波的干扰具有较好的鲁棒性.

一种基于Cauchy分布的H.264宏块层码率控制算法

提出了一种精确有效的H.264宏块层码率控制算法。首先,基于H.264编码器中DCT系数服从Cauchy分布的假设推导得出H.264宏块层的码率模型;并采用线性模型利用前一帧已知的失真度和量化参数值对失真度进行预测;最后,在宏块层通过Lagrange方法最小化失真度,确定宏块的量化参数。实验结果显示,与JM8.6中采用的码率控制算法相比,本文算法不仅能提高平均PSNR,而且能显著降低比特数估计偏差,从而提高图像质量,减小比特数波动,更好地跟踪目标码率。

基于Cauchy分布的非下采样Shearlet HMT模型及其图像去噪应用

Shearlet变换作为后小波时代的一个重要的多尺度几何分析工具具有良好的各向异性和方向捕捉性,同时它也可以对诸如图像等多维信号进行一种近最优的稀疏表示.非下采样Shearlet变换(NSST)在保持Shearlet变换特性的同时还具有平移不变特性,这在具有丰富纹理和细节信息的图像处理中发挥着重要作用.该文首先对图像NSST方向子带内系数的概率密度分布进行分析,获得系数的稀疏统计特性和Cauchy分布拟合子带内系数的有效性;其次对NSST方向子带间系数的联合概率分布进行分析,获得方向子带系数间所具有的持续和传递特性,确定了一种NSST子带间树形架构的系数对应关系,进而提出一种NSST域隐马尔可夫模树模型(C-NSSTHMT),该模型通过Cauchy分布来拟合NSST系数,更好地揭示图像NSST变换后相同尺度子带内和不同尺度子带间系数的相关性.进一步提出一种基于所提出C-NSST-HMT模型的图像去噪算法,该算法对于含噪声方差为30和40的噪声图像,其去噪后的PSNR(Peak Signal to Noise Ratio)较NSCT-HMT方法分别提高了1.995dB和1.193dB.特别对纹理和细节丰富的图像,该算法在去噪的同时,有效地保留了图像的几何信息.

一种基于修正Cauchy分布的高分辨率AVO三参数反演方法

AVO三参数波形反演可以同时获取纵、横波反射率和密度反射率3个参数。但AVO三参数反演问题通常是"病态"的,而且不易得到高分辨率的反演结果。该文基于贝叶斯反演理论,结合反褶积数学框架,提出了一种改进的基于修正Cauchy分布的先验约束方法。从数值模拟的结果看,该方法不仅提高了反演的稳定性,很好地恢复了反射率序列的稀疏性,而且较好地保护了弱反射体信息,能够快速得到较可靠的高分辨率反演结果。

基于Cauchy分布模型与NSST变换的图像去噪算法

非下采样Shearlet变换(NSST)具有良好的方向敏感性,各向异性以及平移不变性,是接近最优的多尺度稀疏表示方法.提出一种基于先验柯西(Cauchy)模型的NSST域图像去噪方法,利用Cauchy分布对NSST变换域子带系数概率分布进行拟合,作为先验分布模型,再通过最大后验概率(MAP)方法估计不含噪声的系数.该方法不但保留了传统统计模型去噪方法中的优点,还通过对NSST具有更好拟合效果的柯西分布模型作为先验的概率分布模型,使估计出的系数更接近于原始图像的系数.大量仿真实验验证了所提出方法的有效性.

基于BSCWEformer的退火炉内分组式辊速序列预测

退火炉内带钢的长度受到温度、张力等因素的影响而变化,导致辊的转速改变以及焊缝位置的不确定,从而威胁生产安全.为了准确预测辊的转速以计算焊缝的实时位置,本文提出基于带状稀疏柯西自注意力的BSCWEformer (banded sparse Cauchy weight enhanced Transformer)模型.模型采用带状稀疏的、使用基于相对位置计算的柯西分布权重值增强的自注意力结构,在提高相邻输入序列的重要性的同时,将自注意力的复杂度由二次方降低为线性.通过实际生产数据进行实验,并与LogSparse Transformer、Transformer、RNMT+等模型进行对比,得出本文所提出的BSCWEformer模型在退火炉内分组式辊速序列预测任务上具有较高的预测精度.

双截尾Cauchy分布参数估计的若干问题

设{X_k,1≤k≤n}独立且均服从参数为μ,λ,A,B的双截尾Cauchy分布,得到了矩估计的性质并给出了当参数μ,λ已知时,参数A,B矩估计的数值解法,得出了参数A,B的极大似然估计并证明了其他参数极大似然估计的存在性和强相合性,最后利用极端顺序统计量的渐近分布得到了参数A,B的近似区间估计.