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Likelihood inference for Birnbaum-Saunders frailty model with an application to bone marrow transplant data

JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION 2023年第13期93卷 2158-2175页

作者： Liu, Kai Balakrishnan, N. He, Mu Xie, Lingfang Shanghai Lixin Univ Accounting & Finance Interdisciplinary Res Inst Data Sci Sch Stat & Math Shanghai Peoples R China McMaster Univ Dept Math & Stat Hamilton ON Canada Xian Jiaotong Liverpool Univ Dept Math Sci Suzhou Peoples R China Shanghai Lixin Univ Accounting & Finance Sch Finance Shanghai Peoples R China Shanghai Lixin Univ Accounting & Finance Sch Finance Shanghai 201209 Peoples R China

Cluster failure time data are commonly encountered in survival analysis due to unobservable factors such as shared environmental conditions and genetic similarity. In such cases, careful attention needs to be paid to the correlation among the subjects within the same cluster. In addition, some diseases are curable due to the advancement of modern medical techniques. In this paper, we extend the frailty model based on Birnbaum-Saunders frailty distribution to incorporate the cure proportion. In addition, the marginal likelihood approach using Monte Carlo approximation and expectation-maximization algorithm are also developed for the determination of the maximum likelihood estimates of the parameters of the proposed model. An extensive simulation study is carried out to evaluate the performance of the proposed model and the methods of inference. Finally, the proposed model is applied to a real data set to analyse the effect of allogeneic and autologous bone marrow transplant treatment on acute lymphoblastic leukemia patients.

关键词： Survival analysis censored data cluster data failure time data frailty model mixture cure frailty model Birnbaum-Saunders distribution monte carlo simulation marginal likelihood approach expectation-maximization method

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method of Noise-Robust Estimation of Parameters of an Autoregressive Model in the Frequency Domain

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CYBERNETICS AND SYSTEMS ANALYSIS 2021年第5期57卷 836-842页

作者： Zadiraka, V. K. Semenov, V. Yu. Semenova, Ye. V. Natl Acad Sci Ukraine V M Glushkov Inst Cybernet Kiev Ukraine Kyiv Acad Univ Kiev Ukraine LLC Delta SPE Kiev Ukraine Natl Acad Sci Ukraine Inst Math Kiev Ukraine Kyiv Acad Univ Kiev Ukraine

The article considers the problem of estimating the parameters of the autoregressive (AR) signal in the presence of background noise. Based on the frequency representation of the AR signal, a technique of calculating the likelihood function of the AR parameters is shown and the implementation of the expectation-maximization method for iterative evaluation of the AR parameters is considered. Analysis of different measures of distortion of speech signals shows that the proposed approaches in the frequency domain have the same accuracy as the corresponding approaches in the time domain, but are characterized by significantly lower computing costs.

关键词： autoregressive model likelihood function expectation-maximization method fast Fourier transform

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A Novel Robust Kalman Filter Algorithm With Unknown Noise Statistics for SINS/GPS Integrated Navigation

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中国机械工程学刊 2023年第1期44卷 49-58页

作者： Xin Lai Fu-Xin Yang

The estimation of process noise is often inaccurate due to factors such as ambient noise and maneuvering during carrier motion in practical applications. The higher precision and stability of integrated navigation system are pivotal for systems such as vehicles and aircraft. To solve the problem of uncertainty of unknown parameter estimation in the integrated navigation system, a novel robust expectation-maximization based Kalman filter (EMKF) algorithm is proposed in the paper. The prediction error covariance matrix and measurement noise covariance matrix are estimated adaptively using the online expectation-maximization (EM) method. The remarkable advantages of the EM-KF algorithm in this paper include the following. The online EM is employed to calculate maximum likelihood (ML) estimation of parameters, which converges in each iteration with successive likelihood increments, ensuring local convergence of the iterations; Adaptive estimation of the prediction error covariance matrix and measurement noise covariance matrix can reduce the reliance on prior information and the influence of noise on measurement estimates; The proposed EMKF has remarkable navigation and robustness in complex environment. Simulations and the field test results illustrate that the proposed EM-KF algorithm has strong robustness and accuracy, and enables high accuracy navigation of moving vehicles for a long time.

关键词： expectation-maximization method Kalman filter Uncertain noise Strapdown inertial navigation system Global positioning system Integrated navigation

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EM Based Parameter Estimation for Markov Modulated Fluid Arrival Processes 1

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26th International Conference, on Analytical and Stochastic Modelling Techniques and Applications (ASMTA)

作者： Almousa, Salah Al-Deen Horvath, Gabor Telek, Miklos Budapest Univ Technol & Econ Dept Networked Syst & Serv Budapest Hungary ELKH MTA BME Informat Syst Res Grp Budapest Hungary

ISBN: (数字)9783030918255

ISBN: (纸本)9783030918255;9783030918248

关键词： Markov modulated fluid arrival processes expectation-maximization method Parameter estimation

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A new and reliable intelligent model for deployment of sensor nodes for IoT applications

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COMPUTERS & ELECTRICAL ENGINEERING 2022年 101卷

作者： Dash, Ranjan Kumar Cengiz, Korhan Alshehri, Yasser Ali Alnazzawi, Noha Coll Engn & Technol Dept IT Bhubnaeswar Orissa India Trakya Univ Dept Elect Elect Engn TR-22030 Edirne Turkey Univ Fujairah Coll Informat Technol Fujairah U Arab Emirates Yanbu Univ Coll Royal Commiss Comp Sci & Engn Dept Educ Div Yanbu Saudi Arabia

To ensure reliable Internet of Things (IoT) applications, this study provides a new intelligent deployment technique for sensor nodes. The proposed intelligent deployment technique places sensor nodes in appropriate locations to achieve high levels of connectivity and, as a result, boost the WSN's overall reliability. To generate optimal sensor node coordinate positions, a modified version of the expectation-maximization method is utilized as a machine learning technique. Following that, each node's neighbours are determined, and links between them are built using the K-Nearest neighbour algorithm. Then, to assess the reliability of WSN, a novel algorithm is proposed. The proposed algorithms are all well-illustrated with appropriate examples. When the algorithms provided in this research are compared to certain existing methods in terms of node positioning accuracy (10%), network connectivity (10%), and estimated dependability values (5%), it is clear that the suggested strategy outperforms them in every way.

关键词： Internet of Things Wireless sensor network Reliability Connectivity and coverage Optimal node placement Machine earning expectation-maximization method Intelligent deployment

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Non-rigid point set registration based on local neighborhood information support

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PATTERN RECOGNITION 2022年 132卷

作者： Liu, Chuanju Niu, Dongmei Wang, Peng Zhao, Xiuyang Yang, Bo Zhang, Caiming Univ Jinan Shandong Prov Key Lab Network based Intelligent Co Jinan 250022 Peoples R China Shandong Univ Sch Software Jinan 250001 Peoples R China

Non-rigid point set registration is a crucial task and an unsolved problem in the field of computer vi-sion. One commonly used method for solving the problem is based on the Gaussian mixture model (GMM). In this method, the point set registration is formalized as a probability density estimation prob-lem. Most GMM-based methods achieve registration by maintaining global and local structures of points. However, the previous methods did not filter the neighborhood information in the local structure, and the quality of local neighborhood information directly affects the accuracy of registration. Therefore, extracting effective local neighborhood information is still a challenge. We propose a novel point set registration method based on GMM by extracting local neighborhood information. The two point sets X and Y are regarded as the centroids of GMM and data points produced by GMM, respectively. Our method computes initial correspondences by comparing the feature descriptors of point sets, and the ini-tial correspondences are updated by considering the neighborhood information. Our method then uses the expectation-maximization method to solve the GMM. In the experimental results, the efficiency and advantages of our method relative to the current methods are verified by applying five commonly used datasets.(c) 2022 Elsevier Ltd. All rights reserved.

关键词： Non -rigid point set registration Gaussian mixture model expectation-maximization method Local neighborhood information

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Parameter estimation of Markov modulated fluid arrival processes

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PERFORMANCE EVALUATION 2022年 157卷

作者： Almousa, Salah Al-Deen Horvath, Gabor Telek, Miklos Budapest Univ Technol & Econ Dept Networked Syst & Serv Budapest Hungary ELKH BME Informat Syst Res Grp Budapest Hungary

Markov modulated discrete arrival processes have a wide literature, including parameter estimation methods based on expectation-maximization (EM). In this paper, we investigate the adaptation of these EM based methods to Markov modulated fluid arrival processes (MMFAP), and conclude that only the generator matrix of the modulating Markov chain of MMFAPs can be approximated by EM based method. For the rest of the parameters, the fluid rates and the fluid variances, we investigate the efficiency of numerical likelihood *** reduce the computational complexity of the likelihood computation, we accelerate the numerical inverse Laplace transformation step of the procedure with function fitting. (c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Markov modulated fluid arrival processes expectation-maximization method Parameter estimation

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Parameter Estimation For Bursty-Intermittent Observations 28

Parameter Estimation For Bursty-Intermittent Observations

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28th Signal Processing and Communications Applications Conference (SIU)

作者： Candan, Cagaty Orta Dogu Tekn Univ Elekt Elekt Muhendisligi Bolumu Ankara Turkey

ISBN: (纸本)9781728172064

Parameter estimation problem is examined in the setting where the noise power is allowed to change from sample to sample. Parameters of the noise source is assumed to be generated by a Markov chain whose state sequence is not known by the observation system. expectation-maximization algorithm is applied for the estimation of desired parameter with the inclusion of unknown state vector of the Markov chain realization as a latent variable. The suggested scheme can be utilized in applications with bursty noise and/or intermittent signals.

关键词： Parameter estimation Hidden Markov models expectation-maximization method Cramer-Rao bound

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Non-Rigid Point Set Registration Based on New Shape Context and Local Structure Constraint 2020

Non-Rigid Point Set Registration Based on New Shape Context ...

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Proceedings of the 2020 9th International Conference on Computing and Pattern Recognition

作者： Chuanju Liu Xiangyu Kong Xiuyang Zhao School of Information Science and Engineering University of Jinan Jinan Shandong China

ISBN: (纸本)9781450387835

Point set registration is an important issue in many fields. The point set registration methods based on Gaussian mixture models (GMM) is popular in recent years. In this kind of method, the two point sets are regarded as the centroids of GMM and data points, respectively. In this paper, we use a new shape context to assign the membership probabilities of GMM and the expectation-maximization method is used to solve GMM. To improve the precision of transformation parameters, we add the local structure constraint in our method. Experimental results on several datasets show the advantages of the proposed method.

关键词： Point set registration gaussian mixture models structure constraints expectation-maximization method

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BSBL-Based DOA and Polarization Estimation with Linear Spatially Separated Polarization Sensitive Array

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WIRELESS PERSONAL COMMUNICATIONS 2019年第3期109卷 2051-2065页

作者： Li, Binbin Bai, Weixiong Zheng, Guimei He, Xingyu Xue, Bin Zhang, Mingliang Air Force Engn Univ Air & Missile Def Coll Xian 710051 Shaanxi Peoples R China Unit 93557 PLA Beijing Peoples R China

The problem of multiple incident signals' direction of arrival (DOA) and polarization estimation with linear spatially separated polarization sensitive array (SS-PSA) is investigated in sparse Bayesian learning (SBL) framework. SS-PSA is widely studied due to its low mutual coupling compared with the spatially collocated polarization sensitive array. On the one hand, the sparse representation of data model of proposed array can be expressed skillfully as a block-sparse representation. On the other hand, block sparse Bayesian learning (BSBL) algorithm has excellent performance in recovering the block-sparse signals. Therefore, in this paper, BSBL algorithm is extended to SS-PSA for DOA and polarization estimation. Firstly, a sparse representation model without parameters coupling is established, where the sparse coefficient vector is a block-sparse signal. Secondly, the expectation-maximization method in BSBL framework, i.e., BSBL-EM algorithm, is proposed to recover the block-sparse signal. Lastly, angles estimation can be obtained from the recovered sparse signal according to the intra-block correlation. Simulation results demonstrate that the BSBL-EM algorithm used in DOA and polarization estimation with SS-PSA exhibits better performance compared with Block Orthogonal Matching Pursuit algorithm and Group Basis Pursuit.

关键词： Polarization sensitive array Mutual coupling expectation-maximization method Sparse Bayesian learning DOA estimation Polarization estimation

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