A Semiparametric Approach for Modeling Partially Linear Autoregressive Model with Skew Normal Innovations by Leila Sakhabakhsh, Rahman Farnoosh, Afshin Fallah, Mohammadhassan Behzadi

“…Then, the conditional maximum-likelihood approach is used to estimate the unknown parameters through the expectation-maximization (EM) algorithm. Some asymptotic properties for the semiparametric method are established. …”
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Improved and efficient EM channel estimation algorithm for MIMO-OFDM systems by XU Peng1, WANG Jin-kuan1, QI Feng2

“…For multiple-input multiple-output with orthogonal frequency division multiplexing（MIMO-OFDM） systems,the error floor（EF） phenomenon at high signal noise rate（SNR） was induced by the expectation maximum（EM） channel estimation algorithm.In addition,the data transmission efficiency was declined obviously with the increasing number of transmit antennas.According to these problems,an improved and efficient EM channel estimation algorithm was pro-posed.Firstly,an accurate and equivalent signal model was introduced to derive a modified EM algorithm,which im-proved the estimation performance at high SNR.Next,to enhance the data transmission efficiency and further the esti-mate performance of the proposed algorithm,phase orthogonal pilots sequences and joint estimation were carried out over multiple OFDM symbols respectively.Simulation results show that the proposed algorithm has better estimation performance and higher data transmission efficiency.…”
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Blind audio watermarking mechanism based on variational Bayesian learning by Xin TANG, Zhao-feng MA, Xin-xin NIU, Yi-xian YANG

“…In order to improve the performance of audio watermarking detection,a blind audio watermarking mechanism using the statistical characteristics based on MFCC features of audio frames was proposed.The spread spectrum watermarking was embedded in the DCT coefficients of audio frames.MFCC features extracted from watermarked audio frames as well as un-watermarked ones were trained to establish their Gaussian mixture models and to estimate the parameters by vatiational Bayesian learning method respectively.The watermarking was detected according to the maximum likelihood principle.The experimental results show that our method can lower the false detection rate compared with the method using EM algorithm when the audio signal was under noise and malicious attacks.Also,the experiments show that the proposed method achieves better performance in handling insufficient training data as well as getting rid of over-fitting problem.…”
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Array amplitude-phase and mutual coupling error joint correction method based on sparse Bayesian by Ding WANG, Weigang GAO, Zhidong WU

“…In the actual array direction finding system, there are often a variety of errors such as amplitude and phase, mutual coupling, which lead to serious deterioration of array direction finding performance.In order to solve the problem of array direction finding misalignment in the presence of low signal-to-noise ratio, small snapshots and multiple errors, the spatial sparsity of signals were introduced, and Bayesian sparse reconstruction technology was used to solve the passive correction and joint estimation of array signal azimuth in the presence of amplitude-phase and mutual coupling errors.The over-complete model of the received signal with error was constructed, and the posterior probability density function of the received signal was obtained.The EM algorithm was used to iteratively optimize the probability density function to solve the corresponding parameters.At the same time, the CRLB of array error and signal azimuth was derived, and by experimental simulation verifies the effectiveness of the proposed method.…”
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Estimating the Proportion of True Null Hypotheses in Multiple Testing Problems by Oluyemi Oyeniran, Hanfeng Chen

“…The nonparametric likelihood is proposed to be restricted to multinomial models and an EM algorithm is also developed to approximate the estimate of π0. …”
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Markov Regime Switching of Stochastic Volatility Lévy Model on Approximation Mode by Arthit Intarasit

“…Regime switching of stochastic volatility Lévy process is employed in an approximation mode for model calibration and the calibration of parameters model done based on EM algorithm. Finally, some empirical results are illustrated via applications to the Bangkok Stock Exchange of Thailand index.…”
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A Nonlinear Prognostic Model Based on the Wiener Process with Three Sources of Uncertainty by Huifang Niu, Jianchao Zeng, Hui Shi, Bin Wang, Tianye Liu

“…Model parameters are then obtained by the expectation maximization (EM) algorithm, and the drift parameter is estimated adaptively using a Bayesian procedure. …”
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Mixed Platoon Flow Dispersion Model Based on Speed-Truncated Gaussian Mixture Distribution by Weitiao Wu, Wenzhou Jin, Luou Shen

“…Expectation maximum (EM) algorithm was used for parameters estimation. The relationship between the arriving flow distribution at downstream intersection and the departing flow distribution at upstream intersection was investigated using the proposed model. …”
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Using Statistical Model to Study the Daily Closing Price Index in the Kingdom of Saudi Arabia (KSA) by Hassan M. Aljohani, Azhari A. Elhag

“…The expectation-maximization (EM) algorithm converged in 2 repetitions. The data source is from Tadawul KSA.…”
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Finite mixtures of functional graphical models: Uncovering heterogeneous dependencies in high-dimensional data. by Qihai Liu, Kevin H Lee, Hyun Bin Kang

“…We further design an estimation method for MFGM using an iterative Expectation-Maximization (EM) algorithm and functional graphical lasso (fglasso). …”
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Remaining Useful Life Estimation Based on Asynchronous Multisource Monitoring Information Fusion by Yanyan Hu, Shuai Qi, Xiaoling Xue, Kaixiang Peng

“…Also, the unknown model parameters are recursively identified based on the Expectation-Maximization (EM) algorithm with the Generic Algorithm (GA) adopted to solve the maximization problem. …”
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Analysis of Joint Angular Distribution for Nonreciprocal Beams via the Mixture of Gaussian Distribution Based on Ray-Tracing by Jiachi Zhang, Liu Liu, Zhenhui Tan, Kai Wang, Tao Zhou

“…Furthermore, to characterize the relationship between quasiangles of departure (AoD) and quasiangles of arrival (AoA), the Gaussian mixture model (GMM) is adopted and the expectation-maximization (EM) algorithm is used to estimate the unknown parameters of GMM. …”
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Predicting Disease Onset from Mutation Status Using Proband and Relative Data with Applications to Huntington's Disease by Tianle Chen, Yuanjia Wang, Yanyuan Ma, Karen Marder, Douglas R. Langbehn

“…In this work, we use the expectation-maximization (EM) algorithm to handle the missing huntingtin gene information in first-degree family members in COHORT, assuming that a family member has the same CAG length as the proband if the family member carries a huntingtin gene mutation. …”
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Causation Analysis of Hazardous Material Road Transportation Accidents by Bayesian Network Using Genie by Xiaoli Ma, Yingying Xing, Jian Lu

“…Parameter learning was conducted by the Expectation-Maximization (EM) algorithm in Genie 2.0. The two main results could be likely to obtain the following. (1) The Bayesian network model can explore the most probable factor or combination leading to the accident, which calculated the posterior probability of each risk factor. …”
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A pipeline for processing hyperspectral images, with a case of melanin-containing barley grains as an example by I. D. Busov, M. A. Genaev, E. G. Komyshev, V. S. Koval, T. E. Zykova, A. Y. Glagoleva, D. A. Afonnikov

“…The current version of the package implements the following methods: construction of a confidence interval of an arbitrary level for the difference of sample averages; verification of the similarity of intensity distributions of spectral lines for two sets of hyperspectral images on the basis of the Mann–Whitney U-criterion and Pearson’s criterion of agreement; visualization in two-dimensional space using dimensionality reduction methods PCA, ISOMAP and UMAP; classification using linear or ridge regression, random forest and catboost; clustering of samples using the EM-algorithm. The software pipeline is implemented in Python using the Pandas, NumPy, OpenCV, SciPy, Sklearn, Umap, CatBoost and Plotly libraries. …”
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A quasi affine transformation evolution algorithm with evolution matrix selection operation for parameter estimation of proton exchange membrane fuel cells by Mohammad Aljaidi, Pradeep Jangir, Sunilkumar P. Agrawal, Sundaram B. Pandya, Anil Parmar, Samar Hussni Anbarkhan, Laith Abualigah

“…Results show that the QUATRE-EMS algorithm reduces SSE significantly, with an average SSE of 0.078492, which is 15% less than the best performing existing algorithms. …”
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Comparison of principal component analysis algorithms for imputation in agrometeorological data in high dimension and reduced sample size. by Valter Cesar de Souza, Sergio Augusto Rodrigues, Luís Roberto Almeida Gabriel Filho

“…The aim of this study was to evaluate the performance of alternative multivariate procedures for principal component analysis (PCA), using the Nonlinear Iterative Partial Least Squares (NIPALS) and Expectation-Maximization (EM) algorithms, for imputing missing data in time series of meteorological variables. …”
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High Performance Computing of Complex Electromagnetic Algorithms Based on GPU/CPU Heterogeneous Platform and Its Applications to EM Scattering and Multilayered Medium Structure by Zhe Song, Xing Mu, Hou-Xing Zhou

“…Comparing to the exploration in EM algorithms from mathematical point of view, the computer programming realization is coordinately significant while keeping up with the development of hardware architectures. …”
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