检索结果-内蒙古大学图书馆

estimating the bivariate mean vector of censored environmental data with Box-Cox transformations and e-m algorithm

eNVIRONmeTRICS 2006年第4期17卷 405-416页

作者： Freeman, Jade modarres, Reza George Washington Univ Dept Stat Washington DC 20052 USA US EPA Off Water Washington DC 20460 USA

We present a method for estimating the mean vector from a bivariate skew distribution that includes some unobserved data below the detection limits. The method uses a Box-Cox transformation, of which the parameters are found by maximizing the likelihood function over a fixed power transformation set. To estimate the mean vector and the covariance matrix, we develop an e-m algorithm solution. Given a transformation, we obtain expressions for the mean vector, covariance matrix, and the asymptotic covariance of the vector of means in the original scale. expressions are obtained for a confidence region for the vector of means. The performance of the maximum likelihood estimation (mLe) method in selecting the correct power transformation and the coverage rate of the confidence region under several conditions are investigated in a simulation study. This method gives reliable results for finding effective transformations and the coverage rate for highly skew data sets. The method is applied to water quality monitoring data. Copyright (c) 2005 John Wiley & Sons, Ltd.

关键词： Box-Cox transformation censored data confidence region delta-method e-m algorithm maximum likelihood

来源：评论

学校读者我要写书评

暂无评论

Identification of the focal plane wavefront control system using e-m algorithm 8

Identification of the focal plane wavefront control system u...

引用

Conference on Techniques and Instrumentation for Detection of exoplanets VIII

作者： Sun, He Kasdin, N. Jeremy Vanderbei, Robert Princeton Univ Princeton NJ 08544 USA

ISBN: (数字)9781510612587

ISBN: (纸本)9781510612587;9781510612570

In a typical focal plane wavefront control (FPWC) system, such as the adaptive optics system of NASA's WFIRST mission, the efficient controllers and estimators in use are usually model-based. As a result, the modeling accuracy of the system influences the ultimate performance of the control and estimation. Currently, a linear state space model is used and calculated based on lab measurements using Fourier optics. Although the physical model is clearly de fined, it is usually biased due to incorrect distance measurements, imperfect diagnoses of the optical aberrations, and our lack of knowledge of the deformable mirrors (actuator gains and influence functions). In this paper, we present a new approach for measuring/estimating the linear state space model of a FPWC system using the expectation-maximization (e-m) algorithm. Simulation and lab results in the Princeton's High Contrast Imaging Lab (HCIL) show that the e-m algorithm can well handle both the amplitude and phase errors and accurately recover the system. Using the recovered state space model, the controller creates dark holes with faster speed. The final accuracy of the model depends on the amount of data used for learning.

关键词： exoplanet direct imaging coronagraph wavefront correction e-m algorithm system identification statistical learning

来源：评论

学校读者我要写书评

暂无评论

Isotontic regression for interval censored survival data using an e-m algorithm

引用

Communications in Statistics - Theory and methods 1986年第8期15卷 2493-2505页

This paper develops a nonparametric model of the relationship between survival S and a dichotomous random variable X under the order constraint that P(X=1|S=s) is increasing (or decreasing) with s. The estimation procedure, called isotonic regression, has been studied in some depth for the case of uncensored data, but we give a methodology which is appropriate in the more general context of right, left, and interval censored data. An e-m algorithm (Dempster et. al., 1977) is used for maximum likelihood estimation.

关键词： nonparametric isotonic regression order constraint left censored interval censored carcinogenicity e-m algorithm marinnun likelihood survival data

来源：评论

学校读者我要写书评

暂无评论

Partial Deconvolution With Inaccurate Blur Kernel

引用

Ieee TRANSACTIONS ON ImAGe PROCeSSING 2018年第1期27卷 511-524页

作者： Ren, Dongwei Zuo, Wangmeng Zhang, David Xu, Jun Zhang, Lei Harbin Inst Technol Sch Comp Sci & Technol Harbin 150001 Heilongjiang Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Hong Kong Peoples R China

most non-blind deconvolution methods are developed under the error-free kernel assumption, and are not robust to inaccurate blur kernel. Unfortunately, despite the great progress in blind deconvolution, estimation error remains inevitable during blur kernel estimation. Consequently, severe artifacts such as ringing effects and distortions are likely to be introduced in the non-blind deconvolution stage. In this paper, we tackle this issue by suggesting: 1) a partial map in the Fourier domain for modeling kernel estimation error, and 2) a partial deconvolution model for robust deblurring with inaccurate blur kernel. The partial map is constructed by detecting the reliable Fourier entries of estimated blur kernel. And partial deconvolution is applied to wavelet-based and learning-based models to suppress the adverse effect of kernel estimation error. Furthermore, an e-m algorithm is developed for estimating the partial map and recovering the latent sharp image alternatively. experimental results show that our partial deconvolution model is effective in relieving artifacts caused by inaccurate blur kernel, and can achieve favorable deblurring quality on synthetic and real blurry images.

关键词： Image deblurring blind deconvolution blur kernel estimation e-m algorithm

来源：评论

学校读者我要写书评

暂无评论

empirical Bayes estimation for combinations of multivariate bioassays

引用

BIOmeTRICS 1999年第4期55卷 1038-1043页

作者： Chen, DG Carter, em Hubert, JJ Kim, PT Fisheries & Oceans Canada Pacific Biol Stn Nanaimo BC V9R 5K6 Canada Univ Guelph Dept Math & Stat Guelph ON N1G 2W1 Canada

This article presents a new empirical Bayes estimator (eBe) and a shrinkage estimator for determining the relative potency from several multivariate bioassays by incorporating prior information on the model parameters based on Jeffreys' rules. The eBe can account for any extra variability among the bioassays, and if this extra variability is 0, then the eBe reduces to the maximum likelihood estimator for combinations of multivariate bioassays. The shrinkage estimator turns out to be a compromise of the prior information and the estimator from each multivariate bioassay, with the weights depending on the prior variance.

关键词： e-m algorithm Jeffreys' rules posterior distribution prior information relative potency

来源：评论

学校读者我要写书评

暂无评论

Classification of pixels in a noisy grayscale image of polar ice

引用

Ieee TRANSACTIONS ON GeOSCIeNCe AND RemOTe SeNSING 2002年第8期40卷 1879-1884页

作者： Das Peddada, S Hwang, JTG Univ Virginia Dept Stat Charlottesville VA 22903 USA Cornell Univ Dept Math Ithaca NY 14853 USA

Often, in synthetic aperture radar (SAR) images of polar ice, one encounters shadow-like features across the images. Such features make it difficult to classify pixels into ice and water. Accordingly, it becomes a challenge to determine the true size and boundaries of ice floes in an SAR image of polar ice. In this paper, we develop a simple statistical procedure which classifies pixels of an image by eliminating the effects of shadow-like features. methodology developed in this paper is illustrated using some noisy SAR images of ice floes in the Arctic sea.

关键词： binarization e-m algorithm monte Carlo markov chain (mCmC) shadow-like features

来源：评论

学校读者我要写书评

暂无评论

evaluating latent class analysis models in qualitative phenotype identification

引用

COmPUTATIONAL STATISTICS & DATA ANALYSIS 2006年第4期50卷 1090-1104页

作者： Yang, CC Natl Taichung Teachers Coll Grad Sch Educ Measurement & Stat Taichung 403 Taiwan

The paper is aimed to investigate the performance of information criteria in selecting latent class analysis models which are often used in research of phenotype identification. Six information criteria and a sample size adjustment (Psychometrika 52 (1987) 333) are compared under various sample sizes and model dimensionalities. The simulation design is particularly meaningful for phenotypic research in practice. Results show that improvements by the sample size adjustment are considerable. In addition, the sample size and model dimensionality effects are found to be influential in the simulation study. (c) 2004 elsevier B.V. All rights reserved.

关键词： phenotype identifications latent class analysis information criteria model selections e-m algorithm

来源：评论

学校读者我要写书评

暂无评论

Coupled dynamics with an external system and application to international finance

引用

PHYSICA A-STATISTICAL meCHANICS AND ITS APPLICATIONS 2019年 520卷 409-432页

作者： Basak, Gopal K. Das, Pranab Kumar Rohit, Allena Indian Stat Inst Stat Math Unit Kolkata 700108 India Ctr Studies Social Sci R1 BP Township Kolkata 700094 India Emory Univ Goizueta Business Sch Atlanta GA 30322 USA

The paper develops a generalized model of coupled dynamics for addressing the choice theoretic problems of economics and other behavioural sciences. The model extends the framework of coupled system to include an external sector that generates a richer dynamics. The model is applied to explain foreign capital inflow from the developed to the developing countries. Under certain regularity conditions, the existence of the solutions to the dynamic choice problem is proved and they are then obtained by numerical technique because of the non-linearity of the related functions. Robustness results are achieved via additional simulations by perturbation of the baseline parameter values. (C) 2019 elsevier B.V. All rights reserved.

关键词： Coupled dynamics Capital inflow Dynamic programming principle Portfolio theory e-m algorithm Dynamic terms of trade

来源：评论

学校读者我要写书评

暂无评论

Linear and nonlinear image deblurring: A documented study

引用

SIAm JOURNAL ON NUmeRICAL ANALYSIS 1999年第6期36卷 1659-1689页

作者： Carasso, AS Natl Inst Stand & Technol Div Math & Computat Sci Gaithersburg MD 20899 USA

Nonlinear image deblurring procedures based on probabilistic considerations are widely believed to outperform conventional linear methods. This paper is exclusively concerned with nonsmooth images such as those that occur in biomedical imaging, where reconstruction of high frequency detail is of prime interest, and where avoidance of a priori smoothness constraints is a major concern. The theoretical basis behind each of the following nonlinear procedures is examined: the Lucy-Richardson method, the maximum likelihood e-m algorithm, the Poisson maximum a posteriori method, and the Nunez-Llacer version of the maximum entropy method. A linear iterative method, VanCittert's iteration, is also studied. It is shown that each of the first three methods, as well as VanCittert's method, lack a necessary ingredient for successful solution of the ill-posed deblurring problem, while in the maximum entropy method, the enforced smoothness may have adverse consequences in medical imaging. A direct linear method, the slow evolution from the continuation boundary (SeCB) method, designed specifically for nonsmooth images, is also considered. That method is stabilized by constraining the blurring operator as well as the solution and does not require smoothness constraints. It is shown that useful error estimates can be obtained in the SeCB method while this is impossible in Tikhonov's method without a priori bounds on derivatives of the unknown solution. Reconstruction experiments on low noise synthetic mRI data show that thousands of iterations are necessary to achieve sufficient resolution in the iterative procedures. However, the SeCB method provides higher resolution at considerable savings in computer time. At high noise levels, the iterative algorithms are shown to diverge. At these same noise levels, the SeCB method produces reconstructions comparable in quality to those that would be obtained in the iterative methods, were one able to terminate the divergent algorithm at that

关键词： image deblurring nonsmooth images biomedical imaging Bayesian methods Poisson noise maximum likelihood maximum a posteriori maximum entropy divergent algorithms Lucy-Richardson method e-m algorithm VanCittert's method slow evolution from the continuation boundary method error bounds numerical experiments

来源：评论

学校读者我要写书评

暂无评论

Identification and adaptive control of a high-contrast focal plane wavefront correction system

引用

JOURNAL OF ASTRONOmICAL TeLeSCOPeS INSTRUmeNTS AND SYSTemS 2018年第4期4卷

作者： Sun, He Kasdin, N. Jeremy Vanderbei, Robert Princeton Univ Dept Mech & Aerosp Engn Princeton NJ 08544 USA Princeton Univ Dept Operat Res & Financial Engn Princeton NJ 08544 USA

All coronagraphic instruments for exoplanet high-contrast imaging need wavefront correction systems to reject optical aberrations and create sufficiently dark holes. Since the most efficient wavefront correction algorithms (controllers and estimators) are usually model-based, the modeling accuracy of the system influences the ultimate wavefront correction performance. Currently, wavefront correction systems are typically approximated as linear systems using Fourier optics. However, the Fourier optics model is usually biased due to inaccuracies in the layout measurements, the imperfect diagnoses of inherent optical aberrations, and a lack of knowledge of the deformable mirrors (actuator gains and influence functions). moreover, the telescope optical system varies over time because of instrument instabilities and environmental effects. We present an expectation- maximization (e-m) approach for identifying and real-time adapting the linear telescope model from data. By iterating between the e-step (a Kalman filter and a Rauch smoother) and the m-step (analytical or gradientbased optimization), the algorithm is able to recover the system even if the model depends on the electric fields, which are unmeasurable hidden variables. Simulations and experiments in Princeton's High Contrast Imaging Lab demonstrate that this algorithm improves the model accuracy and increases the efficiency and speed of the wavefront correction. (c) The Authors. Published by SPIe under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

关键词： exoplanet direct imaging coronagraph wavefront correction e-m algorithm system identification adaptive control reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：