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Blind Identification Based on expectation-maximization algorithm Coupled With Blocked Rhee-Glynn Smoothing Estimator

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IEEE COMMUNICATIONS LETTERS 2018年第9期22卷 1838-1841页

作者： Chen, Wenhao Ma, Lu Liang, Xuwen Chinese Acad Sci Shanghai Inst Microsyst & Informat Technol Shanghai 200050 Peoples R China Chinese Acad Sci Shanghai Engn Ctr Microsatellites Shanghai 201210 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Shanghai SpaceOK Aerosp Technol Co Ltd Shanghai 201802 Peoples R China

In this letter, we consider blind estimation of channel parameters over a frequency-selective channel. We use a blocked Rhee-Glynn smoothing estimator to derive E-step in the expectation-maximization (EM) algorithm. The proposed algorithm copes with the curse of dimensionality of a forward- backward algorithm;meanwhile, it is easy to parallelize, which is amenable to a modern computing hardware and speeds up the estimation of channel parameters. The experiment results show that the proposed algorithm is close to the Baum-Welch algorithm in terms of convergence of channel coefficients and outperforms the EM algorithm coupled with a joined two-filter smoothing algorithm in terms of convergence of channel coefficients and running time.

关键词： Maximum likelihood estimation multipath channels hidden Markov models Rhee-Clynn estimator expectation-maximization algorithm

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Parameter estimation from load-sharing system data using the expectation-maximization algorithm

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IIE TRANSACTIONS 2013年第2期45卷 147-163页

作者： Park, Chanseok Clemson Univ Dept Math Sci Clemson SC 29634 USA

This article considers a system of multiple components connected in parallel. As components fail one by one, the remaining working components share the total load applied to the system. This is commonly referred to as load sharing in the reliability engineering literature. This article considers the traditional approach to the modeling of a load-sharing system under the assumption of the existence of underlying hypothetical latent random variables. Using the expectation-maximization (EM) algorithm, a methodology is proposed to obtain the maximum likelihood estimates in such a model in the case where the underlying lifetime distribution of the components is lognormal or normal. The proposed EM method is also illustrated and substantiated using numerical examples. The estimates obtained using the EM algorithm are compared with those obtained using the Broyden-Fletcher-Goldfarb-Shanno algorithm, which falls under the class of numerical methods known as Newton or quasi-Newton methods. The results show that the estimates obtained using the proposed EM method always converge to a unique global maximizer, whereas the estimates obtained using the Newton-type method are highly sensitive to the choice of starting values and thus often fail to converge.

关键词： Broyden-Fletcher-Goldfarb-Shanno algorithm expectation-maximization algorithm load sharing hypothetical latent random variable maximum likelihood estimate lognormal distribution

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Estimation of void boundaries in flow field using expectation-maximization algorithm

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CHEMICAL ENGINEERING SCIENCE 2011年第3期66卷 355-374页

作者： Khambampati, Anil Kumar Rashid, Ahmar Lee, Jeong Seong Kim, Bong Seok Liu, Dong Kim, Sin Kim, Kyung Youn Jeju Natl Univ Dept Elect Engn Cheju 690756 South Korea Jeju Natl Univ Clean Energy Training & Educ Ctr BK21 Cheju 690756 South Korea Jeju Natl Univ Appl Radiol Sci Res Inst Cheju 690756 South Korea Jeju Natl Univ Dept Nucl & Energy Engn Cheju 690756 South Korea

Phase distribution in the flow field provides an insight in to the hydrodynamics and heat transfer between the fluids. Void fraction, which is one of the key flow parameters, can be determined by estimating the phase boundaries. Electrical impedance tomography (EIT), which has high temporal characteristics, has been used as an imaging modality to estimate the void boundaries, using the prior knowledge of conductivities. The voids formed within the process vessel are not stable and their movement is random in nature, thus dynamic estimation schemes are necessary to track the fast changes. Kalman-type estimators like extended Kalman filter (EKF) assume the knowledge of model parameters, such as the initial states, state transition matrix and the covariance of process and measurement noise. In real situations, we do not have the prior information of the model parameters;therefore, in such circumstances the estimation performance of the Kalman-type filters is affected. In this paper, the expectation-maximization (EM) algorithm is used as an inverse algorithm to estimate the model parameters as well as non-stationary void boundary. The uncertainties caused in Kalman-type filters, due to the inaccurate selection of model parameters are over come using an EM algorithm. The performance of the method is tested with numerical and experimental data. The results show that an EM has better estimation of the void boundary as compared to the conventional EKF. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Electrical impedance tomography Two-phase flows Interface estimation State estimation Extended Kalman filter expectation-maximization algorithm

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A joint shape evolution approach to medical image segmentation using expectation-maximization algorithm

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MAGNETIC RESONANCE IMAGING 2011年第9期29卷 1255-1266页

作者： Farzinfar, Mahshid Teoh, Eam Khwang Xue, Zhong Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Cornell Univ Weill Med Coll Methodist Hosp Res Inst Ctr Bioengn & Informat Houston TX 77030 USA

This study proposes an expectation-maximization(EM)-based curve evolution algorithm for segmentation of magnetic resonance brain images. In the proposed algorithm, the evolution curve is constrained not only by a shape-based statistical model but also by a hidden variable model from image observation. The hidden variable model herein is defined by the local voxel labeling, which is unknown and estimated by the expected likelihood function derived from the image data and prior anatomical knowledge. In the M-step, the shapes of the structures are estimated jointly by encoding the hidden variable model and the statistical prior model obtained from the training stage. In the E-step, the expected observation likelihood and the prior distribution of the hidden variables are estimated. In experiments, the proposed automatic segmentation algorithm is applied to multiple gray nuclei structures such as caudate, putamens and thalamus of three-dimensional magnetic resonance imaging in volunteers and patients. As for the robustness and accuracy of the segmentation algorithm, the results of the proposed EM-joint shape-based algorithm outperformed those obtained using the statistical shape model-based techniques in the same framework and a current state-of-the-art region competition level set method. (C) 2011 Elsevier Inc. All rights reserved.

关键词： Statistical shape model Active contours Level set methods expectation-maximization algorithm

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A quantile variant of the expectation-maximization algorithm and its application to parameter estimation with interval data

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JOURNAL OF algorithmS & COMPUTATIONAL TECHNOLOGY 2018年第3期12卷 253-272页

作者： Park, Chanseok Pusan Natl Univ Dept Ind Engn Busan South Korea

The expectation-maximization algorithm is a powerful computational technique for finding the maximum likelihood estimates for parametric models when the data are not fully observed. The expectation-maximization is best suited for situations where the expectation in each E-step and the maximization in each M-step are straightforward. A difficulty with the implementation of the expectation-maximization algorithm is that each E-step requires the integration of the log-likelihood function in closed form. The explicit integration can be avoided by using what is known as the Monte Carlo expectation-maximization algorithm. The Monte Carlo expectation-maximization uses a random sample to estimate the integral at each E-step. But the problem with the Monte Carlo expectation-maximization is that it often converges to the integral quite slowly and the convergence behavior can also be unstable, which causes computational burden. In this paper, we propose what we refer to as the quantile variant of the expectation-maximization algorithm. We prove that the proposed method has an accuracy of O(1/K-2), while the Monte Carlo expectation-maximization method has an accuracy of O-p (1/root K). Thus, the proposed method possesses faster and more stable convergence properties when compared with the Monte Carlo expectation-maximization algorithm. The improved performance is illustrated through the numerical studies. Several practical examples illustrating its use in interval-censored data problems are also provided.

关键词： expectation-maximization algorithm incomplete data maximum likelihood Monte Carlo expectation-maximization missing data quantile

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Novel optimal time selection method for synthetic aperture radar data processing via expectation-maximization algorithm

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ELECTRONICS LETTERS 2022年第15期58卷 594-596页

作者： Cao, Rui Wang, Yong Harbin Inst Technol Sch Elect & Informat Engn Harbin Peoples R China

A novel optimal time selection method for synthetic aperture radar (SAR) data processing via the expectation-maximization (EM) algorithm is proposed, which can reduce the computational complexity and improve the image quality simultaneously. First, the Doppler frequency feature after the SAR processing is analyzed from the point of probability. Then, the Doppler frequency is modelled as the Gaussian mixture model (GMM), and the issue of optimal time selection can be implemented by the parameter estimation of GMM. The results of simulated and real measured data are given to demonstrate the effectiveness of the proposed method.

关键词： simulated measured data radar imaging SAR data processing Gaussian mixture model expectation-maximization algorithm Gaussian processes Doppler frequency feature image quality real measured data optimal time selection method synthetic aperture radar computational complexity expectation-maximisation algorithm synthetic aperture radar data processing mixture models parameter estimation

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Estimating the size of undeclared work from partially misclassified survey data via the expectation-maximization algorithm

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JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS 2024年第3期73卷 816-834页

作者： Arezzo, Maria Felice Guagnano, Giuseppina Vitale, Domenico Sapienza Univ Rome Dept Methods & Models Econ Terr & Finance I-00161 Rome Italy Sapienza Univ Rome Dept Methods & Models Econ Terr & Finance Via Castro Laurenziano 9 I-00161 Rome Italy

Undeclared work (UW) is pervasive in economies. This explains the interest of public authorities in knowing its size and drivers. Unfortunately, this is a very complex task because several issues often arise in the collected data, due to the sensitivity of the topic. In sample surveys, one major problem is misclassification. Without appropriate adjustments, inference would provide biased estimates, the reason being the concealing of undeclared status. In order to overcome such problem, we developed a methodology based on a expectation-maximization algorithm that accounts for misclassification due to dishonest answering. Through the proposed approach, we are able to estimate the prevalence of UW and its determinants. The reliability of the methodology is validated through an extensive simulation study. An application to the Special Eurobarometer survey no. 402 on UW is provided.

关键词： expectation-maximization algorithm misclassification population prevalence undeclared work

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lclogit: A Stata command for fitting latent-class conditional logit models via the expectation-maximization algorithm

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STATA JOURNAL 2013年第3期13卷 625-639页

作者： Pacifico, Daniele Yoo, Hong Il Italian Dept Treasury Rome Italy Univ Durham Sch Business Durham England

In this article, we describe lclogit, a Stata command for fitting a discrete-mixture or latent-class logit model via the expectation-maximization algorithm.

关键词： st0312 lclogit lclogitpr lclogitcov lclogitml latent-class model expectation-maximization algorithm mixed logit

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A stochastic expectation-maximization algorithm for the analysis of system lifetime data with known signature

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COMPUTATIONAL STATISTICS 2016年第2期31卷 609-641页

作者： Yang, Yandan Ng, Hon Keung Tony Balakrishnan, Narayanaswamy So Methodist Univ Dept Stat Sci Dallas TX 75275 USA McMaster Univ Dept Math & Stat Hamilton ON L8S 4K1 Canada

Statistical estimation of the model parameters of component lifetime distribution based on system lifetime data with known system structure is discussed here. We propose the use of stochastic expectation-maximization (SEM) algorithm for obtaining the maximum likelihood estimates of model parameters based on complete and censored system lifetimes. Different ways of implementing the SEM algorithm are also studied. We have shown that the proposed methods are feasible and are easy to implement for various families of component lifetime distributions. The proposed methodologies are then illustrated with two popular lifetime models-the Weibull and Birnbaum-Saunders distributions. Monte Carlo simulation is then used to compare the performance of the proposed methods with the corresponding estimation by direct maximization. Finally, two illustrative examples are presented along with some concluding remarks.

关键词： expectation-maximization algorithm Maximum likelihood estimation Monte Carlo simulation Reliability data Type-II censoring

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An innovative multimodal/multispectral image registration method for medical images based on the expectation-maximization algorithm 37

An innovative multimodal/multispectral image registration me...

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37th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Arce-Santana, Edgar Campos-Delgado, Daniel U. Mejia-Rodriguez, Aldo Reducindo, Isnardo Univ Autonoma San Luis Potosi Fac Ciencias San Luis Potosi Slp Mexico

ISBN: (纸本)9781424492701

In this paper, we present a methodology for multimodal/multispectral image registration of medical images. This approach is formulated by using the expectation-maximization (EM) methodology, such that we estimate the parameters of a geometric transformation that aligns multimodal/multispectral images. In this framework, the hidden random variables are associated to the intensity relations between the studied images, which allow to compare multispectral intensity values between images of different modalities. The methodology is basically composed by an iterative two-step procedure, where at each step, a new estimation of the joint conditional multispectral intensity distribution and the geometric transformation is computed. The proposed algorithm was tested with different kinds of medical images, and the obtained results show that the proposed methodology can be used to efficiently align multimodal/multispectral medical images.

关键词： expectation-maximisation algorithm image registration medical image processing expectation-maximization algorithm geometric transformation hidden random variables iterative two-step procedure joint conditional multispectral intensity distribution multimodal image registration method multimodal medical images multispectral image registration method multispectral medical images Biomedical imaging Image registration Magnetic resonance imaging Positron emission tomography Single photon emission computed tomography expectation-maximisation algorithm image registration Geometric transformation single photon emission computed tomography Medical imaging Biomedical imaging Medical image processing PET Scan images Magnetic Resonance Imaging

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