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expectation-maximization Estimation algorithm for Bilinear State-space Systems with Missing Outputs Using Kalman Smoother

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INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2023年第3期21卷 912-923页

作者： Wang, Xinyue Ma, Junxia Xiong, Weili Jiangnan Univ Sch Internet Things Engn Key Lab Adv Proc Control Light Ind Minist Educ Wuxi 214122 Peoples R China

In this paper, the parameter estimation of bilinear state-space systems with missing outputs is studied. The bilinear model is transformed into a linear time-varying state-space model, and Kalman smoother with a time-varying gain is adopted to estimate missing outputs and unmeasurable states. Under the expectation-maximization (EM) algorithm scheme, an iterative estimation algorithm based on Kalman smoother is derived, in which the unknown parameters, missing outputs, and unmeasurable states can be estimated simultaneously. Two simulation examples, including a numerical example and a three-tank system experiment, are adopted to verify the effectiveness of the proposed algorithm.

关键词： Bilinear state-space system expectation-maximization algorithm Kalman smoother missing outputs parameter estimation

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expectation-maximization based algorithm for pattern recognition in traffic speed distribution

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MATHEMATICAL AND COMPUTER MODELLING 2013年第1-2期58卷 449-456页

作者： Lo, Shih-Ching Chung Hua Univ Dept Transportat Technol & Logist Management Hsinchu 300 Taiwan

Understanding driving behavior is a complicated research topic. To describe accurate speed, flow and density of a multiclass users traffic flow, an adequate model is needed. Mostly, user-classes are categorized by vehicle type characteristics. However, driving behavior is also influenced by drivers and socio-economic characteristics. Categorizing user-class by vehicle type may not reflect multiclass users traffic flow properly. On the other hand, driving behavior is studied through tracking trace of individual vehicles, experimenting in a driving simulator or inquiring by questionnaire generally. It costs a lot and may produce bias because of the design of the questionnaire or experiment. Therefore, a new method, which is based on a pattern recognition technique, is proposed to classify driving behavior in multiclass users traffic flow. In this study, the speed is considered as the result of driving behavior and the speed distribution on a road is assumed to be a mixture of Gaussian distributions. According to the assumptions, the expectation-maximization algorithm is employed to train and classify different user-classes. With this method, an economical and automatic way for traffic data processing and parameter extraction is obtained. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Signal processing Pattern recognition Gaussian mixture Multiclass users traffic flow expectation-maximization algorithm

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Improved Implementation of expectation maximization algorithm on Graphic Processing Unit 19th

Improved Implementation of Expectation Maximization Algorith...

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19th Chinese Lexical Semantics Workshop (CLSW)

作者： Jing, Si-Yuan Sun, Rui Xie, Chun-Ming Jin, Peng Liu, Yi Liu, Cai-Ming Leshan Normal Univ Sch Comp Sci Leshan 614000 Peoples R China

ISBN: (纸本)9783030040154;9783030040147

In our previous work, an efficient implementation of expectation-maximization (EM) algorithm using CUDA has been proposed for high-speed word alignment. The proposed algorithm can gain a 16.8-fold speedup compared to a multi-thread algorithm and a 234.7-fold speedup compared to a sequential algorithm on a modern graphic processing unit (GPU). In this paper, we try to improve the algorithm to achieve better performance. Through analysis of the previous algorithm, we find that two places in "E" step (expectation calculation) are unreasonably designed. An improved CUDA implementation of the EM algorithm is proposed in this paper. Experimental results show that the new algorithm can improve the speed of expectation calculation by 29.4%.

关键词： Word alignment GPU Parallel computation expectation-maximization algorithm CUDA

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Parameter estimation of the modified Weibull distribution using Monte Carlo expectation maximization algorithm

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Model Assisted Statistics and Applications 2016年第2期11卷 171-178页

作者： Sutar, Santosh Yashwantrao Chavan School of Rural Development Shivaji University Kolhapur 416004 India

In the present article, the modified Weibull distribution proposed by Sarhan and Zaindin [5] is considered and proposed parameter estimation procedure based on the Monte Carlo expectation maximization algorithm to obtain the estimates of unknown parameters. To see the performance of the proposed estimation procedure, we report the simulation results. For illustrative purposes, we have also applied the proposed estimation procedure on a real data set. © 2016 - IOS Press and the authors. All rights reserved.

关键词： expectation-maximization algorithm linear failure rate distribution maximum likelihood estimation modified Weibull distribution Monte Carlo expectation-maximization

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EMA-GAN: A Generative Adversarial Network for Infrared and Visible Image Fusion with Multiscale Attention Network and expectation maximization algorithm

ADVANCED INTELLIGENT SYSTEMS

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ADVANCED INTELLIGENT SYSTEMS 2023年第11期5卷

作者： Xi, Xiuliang Jin, Xin Jiang, Qian Lin, Yu Zhou, Wei Guo, Lei Yunnan Univ Kunming 650091 Yunnan Peoples R China Kunming Phys Inst Kunming 650223 Yunnan Peoples R China

The purpose of the infrared and visible image fusion is to generate a fused image with rich information. Although most fusion methods can achieve good performance, there are still shortcomings in extracting feature information from source images, which make it difficult to balance the thermal radiation region information and texture detail information in the fused image. To address the above issues, an expectation maximization (EM) learning framework based on adversarial generative networks (GAN) for infrared and visible image fusion is proposed. The EM algorithm (EMA) can obtain maximum likelihood estimation for problems with potential variables, which is helpful in solving the problem of lack of labels in infrared and visible image fusion. The axial-corner attention mechanism is designed to capture long-range semantic information and texture information of the visible image. The multifrequency attention mechanism digs the relationships between features at different scales to highlight target information of infrared images in the fused result. Meanwhile, two discriminators are used to balance two different features, and a new loss function is designed to maximize the likelihood estimate of the data with soft class label assignments, which is obtained from the expectation network. Extensive experiments demonstrate the superiority of EMA-GAN over the state-of-the-art.

关键词： attention mechanism expectation-maximization algorithm generative adversarial networks image fusion infrared and visible image fusion

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REMLA: An R package for robust expectation-maximization estimation for latent variable models

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SOFTWAREX 2025年 30卷

作者： Nieser, Kenneth J. Ortiz-Torres, Bryan Saul Zayas-Caban, Gabriel Cochran, Amy VA Palo Alto Hlth Care Syst Ctr Innovat Implementat Menlo Pk CA USA Stanford Univ Stanford Surg Policy Improvement Res & Educ Ctr Dept Surg Stanford CA USA Univ Wisconsin Madison Dept Ind & Syst Engn Madison WI USA Univ Wisconsin Madison Dept Populat Hlth Sci Madison WI USA Univ Wisconsin Madison Dept Math Madison WI USA

Factor analysis is a widely used statistical method for describing a large number of observed, correlated variables in terms of a smaller number of unobserved variables. Applications of this method usually impose the same latent variable model on all individuals in the sample, but this assumption might not hold as individuals can differ in attributes (e.g., age, gender) that influence model parameters. REMLA is an R package that implements a robust expectation-maximization (REM) algorithm to estimate the parameters for factor analysis models in a way that automatically acknowledges, and even detects, differences among individuals within the sample. This paper explains the methodological background of the estimation process, describes the algorithms employed, and illustrates how REMLA can be used to perform exploratory and confirmatory factor analyses through examples. In the future, we plan to extend this package to other latent variable models, such as mixture models.

关键词： expectation-maximization algorithm Robust statistics Structural equation modeling Latent variables Factor analysis

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Fatigue Load Characteristic Modeling Method for Aero-engines Based on the Novel Mixture Distribution

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INTERNATIONAL JOURNAL OF AERONAUTICAL AND SPACE SCIENCES 2025年 1-15页

作者： Yao, Shunyu Niu, Xuming Sun, Zhigang Song, Yingdong Nanjing Univ Aeronaut & Astronaut Coll Energy & Power Engn Minist Ind & Informat Technol Key Lab Aeroengine Thermal Environm & Struct Nanjing 210016 Peoples R China Nanjing Univ Aeronaut & Astronaut State Key Lab Mech & Control Mech Struct Nanjing 210016 Peoples R China Nanjing Univ Aeronaut & Astronaut Wuxi Res Inst Wuxi 214100 Peoples R China Harbin Engn Univ Harbin 150006 Peoples R China

The aero-engine load spectrum is the basis for fatigue life analysis. It is highly challenging to model the fatigue load characteristics in the load spectrum for aero-engines with complex maneuvering loads and numerous extreme operating conditions. In this paper, a parameterized modeling method of the fatigue load characteristics for aero-engines is proposed based on a novel mixture distribution. The novel mixture distribution is composed of hybrid-type components including Weibull-Normal distribution, Gaussian mixture distribution, and Normal-Normal distribution. An improved expectation-maximization algorithm is employed for parameter estimation of the novel mixture distribution model. The results demonstrate that the method proposed improves the fitting accuracy significantly and avoids overfitting. The multi-correlation coefficient reaches 0.9920 and the Kolmogorov-Smirnov errors of marginal distributions are only 0.0090 and 0.0072. Both indicators are superior to those of the mixture distribution composed of single-type components. With the method, the model parameters of the fatigue load characteristics of aero-engines are obtained, providing a basis for the compilation of the aero-engine life test spectrum.

关键词： Aero-engine load spectrum Fatigue Mixture distribution model Parameter estimation expectation-maximization algorithm

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A comparative analysis of several multivariate zero-inflated and zero-modified models with applications in insurance

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COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 2025年第7期54卷 2130-2157页

作者： Zhang, Pengcheng Pitt, David Wu, Xueyuan Shandong Univ Finance & Econ Sch Insurance Jinan Peoples R China Univ Melbourne Ctr Actuarial Studies Dept Econ Melbourne Vic Australia

Given that insurance companies often operate across multiple lines of insurance business, where claim frequencies on different lines are often correlated, it often becomes advantageous to employ multivariate count modeling where dependence between lines of business can be included in the modeling. Due to the operation of bonus-malus systems where there is a reward to the insurance policyholder for not claiming, claims data in automobile insurance often exhibits an excess of common zeros, a characteristic known as multivariate zero-inflation. In this article, we propose two approaches to address this feature. The first approach involves utilizing a multivariate zero-inflated model, where we artificially enhance the probability of common zeros based on standard multivariate count distributions. The second approach applies a multivariate zero-modified model, which separately handles the common zeros and the number of claims incurred in each line given that at least one claim occurs. We present several models under these frameworks, along with detailed inference procedures. In the applications section, we conduct a comprehensive comparative analysis of these models using data from an automobile insurance portfolio.

关键词： Multivariate zero-inflated model multivariate zero-modified model expectation-maximization algorithm minorization-maximization algorithm automobile insurance ratemaking

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On Classical Inference of a Flexible Semi-Parametric Class of Distributions Under a Joint Balanced Progressive Censoring Scheme

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APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY 2025年第1期41卷

作者： Bhattacharyya, Dhrubasish Kundu, Debasis Rajiv Gandhi Inst Petr Technol Dept Math Sci Amethi India Indian Inst Technol Kanpur Dept Math & Stat Kanpur India

The paper deals with the estimation procedures for the proportional hazard class of distributions under a two-sample balanced joint progressive censoring scheme. The baseline hazard function is assumed to be piecewise constant, instead of any specific form. This adds flexibility to the proposed model, and the shape of the underlying hazard function is completely data-driven. Since the complicated form of the likelihood function does not yield closed-form estimators, we propose a variant of the expectation-maximization algorithm, known as the expectation Conditional maximization (ECM) algorithm, for obtaining maximum likelihood estimates of the model parameters. This leads to explicit expressions for the iterative constrained maximization steps of the algorithm. An extension to the case when the cut points are unknown has also been considered for dealing with problems involving real data. Simulation results and illustrations using real data have also been presented.

关键词： bootstrap confidence interval expectation conditional maximization algorithm expectation-maximization algorithm maximum likelihood estimators

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Restoration after deterioration in interdependent infrastructure networks: A two-stage hybrid method with minimum network performance loss

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INFORMATION SCIENCES 2025年 693卷

作者： Li, Yulong Su, Han Yang, Baisong Lin, Jie Shen, Yinghua Wu, Guobin Cent Univ Finance & Econ Sch Management Sci & Engn Beijing 100081 Peoples R China Chongqing Univ Sch Econ & Business Adm Chongqing 400044 Peoples R China

Restoration of interdependent infrastructure networks (IINs) relies on the information from deterioration, which is of great significance because IINs support the normal functioning of social productivity and life. However, existing research has not fully addressed the restoration after deterioration in IINs, which is not conducive to the timely elimination of the adverse effects of infrastructure deterioration. First, a unified model for IINs is innovatively devised by considering both functional and operational interdependencies between infrastructures. Second, a two-stage hybrid method that generates the optimal restoration strategies after deterioration in IINs is proposed. Specifically, in the first stage, a hidden Markov chain model for deterioration prediction is constructed, which is solved by the expectation-maximization (EM) algorithm. In the second stage, an objective programming model with minimum network performance loss for restoration optimization is developed, and the optimal strategy is obtained by the ant colony algorithm. Finally, a real-world case is used to validate the feasibility and effectiveness of the proposed method. The results show that this method is efficient and effective in finding optimal restoration strategy after deterioration in IINs. We also investigate the effects of initial restoration time and restoration resource grouping, which provide helpful decision guidance for real cases.

关键词： Interdependent infrastructure networks Deterioration prediction Restoration optimization expectation-maximization algorithm Ant colony algorithm

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