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Network Traffic Identification Based on Improved em algorithm

IEEE ACCESS 2024年 12卷 26773-26786页

作者： Cui, Hao Liang, Linjie Wang, Jinghui Jilin Univ Architecture & Technol Network Ctr Changchun 130114 Peoples R China Jilin Univ Architecture & Technol Sch Civil Engn Changchun 130114 Peoples R China Jilin Agr Univ Coll Informat Technol Changchun 130118 Peoples R China

With the continuous increase and complexity of network traffic, traditional network traffic recognition technology is facing numerous difficulties, especially in dealing with outlier data and improving recognition accuracy. Therefore, an improved expectation maximization algorithm based on the constraint matrix Z and Tsallis entropy is proposed. The core goal of this algorithm is to accelerate the convergence of classification and improve accuracy. Furthermore, to enhance the classification accuracy, the spatial expectation maximization algorithm is introduced, which innovatively converts the sample mean and covariance matrix into L-1 -median and modified rank covariance matrix. According to the experimental data, the recall rate of the original expectation maximization algorithm is only 74%. However, the recall rate of the spatial expectation maximization algorithm in the Attack service has significantly increased to 85%. In other tests, such as Www and Peer-to-peer services, the recall rate has also significantly improved, increasing from 96% and 95.3% to 97.7% and 96.1%, respectively. These experimental results highlight the superior robustness of the spatial expectation maximization algorithm in handling outlier data. It further proves the outstanding performance in improving the accuracy of network traffic recognition. This research has brought significant innovation and potential practical value to the network traffic identification.

关键词： em algorithm network traffic identification Tsallis entropy restriction matrix

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Estimating transition intensity rate on interval-censored data using semi-parametric with em algorithm approach

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COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 2024年第17期53卷 6038-6054页

作者： Qian, Chen Srivastava, Deo Kumar Pan, Jianmin Hudson, Melissa M. Rai, Shesh N. Univ Louisville James Graham Brown Canc Ctr Biostat & Bioinformat Facil Louisville KY 40202 USA Univ Louisville Dept Biostat & Bioinformat Louisville KY 40202 USA AbbVie Inc N Chicago IL 60064 USA St Jude Childrens Res Hosp Dept Biostat Memphis TN 38105 USA Univ Cincinnati Coll Med Dept Environm & Publ Hlth Sci Div Biostat & Bioinformat Cincinnati OH 45267 USA Univ Cincinnati Coll Med Canc Data Sci Ctr Dept Environm & Publ Hlth Sci Cincinnati OH 45267 USA St Jude Childrens Res Hosp Dept Oncol Memphis TN 38105 USA Univ Cincinnati Biostat & Informat Shared Resources Canc Ctr Cincinnati OH 45267 USA Univ Cincinnati Coll Med Dept Environm & Publ Hlth Sci Div Biostat & Bioinformat Cincinnati OH 45267 USA

Phase IV clinical trials are designed to monitor long-term side effects of medical treatment. For instance, childhood cancer survivors treated with chest radiation and/or anthracycline are often at risk of developing cardiotoxicity during their adulthood. Often the primary focus of a study could be on estimating the cumulative incidence of a particular outcome of interest such as cardiotoxicity. However, it is challenging to evaluate patients continuously and usually, this information is collected through cross-sectional surveys by following patients longitudinally. This leads to interval-censored data since the exact time of the onset of the toxicity is unknown. Rai et al. computed the transition intensity rate using a parametric model and estimated parameters using maximum likelihood approach in an illness-death model. However, such approach may not be suitable if the underlying parametric assumptions do not hold. This manuscript proposes a semi-parametric model, with a logit relationship for the treatment intensities in two groups, to estimate the transition intensity rates within the context of an illness-death model. The estimation of the parameters is done using an em algorithm with profile likelihood. Results from the simulation studies suggest that the proposed approach is easy to implement and yields comparable results to the parametric model.

关键词： Cross-sectional survey data em algorithm interval-censored data phase IV clinical trial profile likelihood semi-parametric

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The em algorithm for variance component estimation in multivariate Fay-Herriot model

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COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION 2024年

作者： Angkunsit, Annop Suntornchost, Jiraphan Chulalongkorn Univ Fac Sci Dept Math & Comp Sci Bangkok Thailand

The multivariate Fay-Herriot model has been shown to be useful in various applications when there are multiple response variables. Therefore, several studies of the model have been pursued specially the study of estimation techniques of the variance components. The two benchmark methods for the variance component estimation are the profile maximum likelihood method and the residual maximum likelihood method. However, it has been shown in literature that these methods can produce zero estimates of the variance components. This leads to unfavorable results since the direct estimates do not contribute to the EBLUPs. In this paper, we propose alternative estimation methods based on the em algorithm for the variance components of the multivariate Fay-Herriot model. In our study, we illustrate the procedures of the em algorithms for the profile likelihood and the residual likelihood. Moreover, we perform a Monte Carlo simulation to investigate the performances of the proposed methods comparing with some existing methods. The simulation results suggest that the em algorithm improves those existing methods in certain cases particularly for the cases of small number of areas which are commonly found in applications. Finally, we apply the proposed estimates to the average household income and average household expenditure in Thailand.

关键词： em algorithm empirical best linear unbiased predictor Multivariate Fay-Herriot Variance component estimation

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Estimating mixed-effects state-space models via particle filters and the em algorithm

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JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION 2024年第11期94卷 2363-2384页

作者： Hamdi, Faycal Lellou, Chahrazed USTHB Fac Math RECITS Lab POB 32 Algiers 16111 Algeria

In this paper, we focus on studying the Mixed-Effects State-Space (MESS) models previously introduced by Liu et al. [Liu D, Lu T, Niu X-F, et al. Mixed-effects state-space models for analysis of longitudinal dynamic systems. Biometrics. 2011;67(2):476-485]. We propose an estimation method by combining the auxiliary particle learning and smoothing approach with the Expectation Maximization (em) algorithm. First, we describe the technical details of the algorithm steps. Then, we evaluate their effectiveness and goodness of fit through a simulation study. Our method requires expressing the posterior distribution for the random effects using a sufficient statistic that can be updated recursively, thus enabling its application to various model formulations including non-Gaussian and nonlinear cases. Finally, we demonstrate the usefulness of our method and its capability to handle the missing data problem through an application to a real dataset.

关键词： Mixed-effects state-space model sequential Monte Carlo method particle filtering em algorithm maximum likelihood estimation

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Bayesian Generalized Linear Models for Analyzing Compositional and Sub-Compositional Microbiome Data via em algorithm

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STATISTICS IN MEDICINE 2025年第7期44卷 e70084页

作者： Zhang, Li Ding, Zhenying Cui, Jinhong Zhou, Xiaoxiao Yi, Nengjun Fox Chase Canc Ctr Biostat & Bioinformat Facil Philadelphia PA 19111 USA Univ Alabama Birmingham Dept Biostat Birmingham AL 35294 USA

The study of compositional microbiome data is critical for exploring the functional roles of microbial communities in human health and disease. Recent advances have shifted from traditional log-ratio transformations of compositional covariates to zero constraint on the sum of the corresponding coefficients. Various approaches, including penalized regression and Markov Chain Monte Carlo (MCMC) algorithms, have been extended to enforce this sum-to-zero constraint. However, these methods exhibit limitations: penalized regression yields only point estimates, limiting uncertainty assessment, while MCMC methods, although reliable, are computationally intensive, particularly in high-dimensional data settings. To address the challenges posed by existing methods, we proposed Bayesian generalized linear models for analyzing compositional and sub-compositional microbiome data. Our model employs a spike-and-slab double-exponential prior on the microbiome coefficients, inducing weak shrinkage on large coefficients and strong shrinkage on irrelevant ones, making it ideal for high-dimensional microbiome data. The sum-to-zero constraint is handled through soft-centers by applying prior distribution on the sum of compositional or subcompositional coefficients. To alleviate computational intensity, we have developed a fast and stable algorithm incorporating expectation-maximization (em) steps into the routine iteratively weighted least squares (IWLS) algorithm for fitting GLMs. The performance of the proposed method was assessed by extensive simulation studies. The simulation results show that our approach outperforms existing methods with higher accuracy of coefficient estimates and lower prediction error. We also applied the proposed method to one microbiome study to find microorganisms linked to inflammatory bowel disease (IBD). The methods have been implemented in a freely available R package BhGLM .

关键词： Bayesian GLMs compositional data em algorithm microbiome spike-and-slab priors sum-to-zero constraint

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Aggregate Markov models in life insurance: estimation via the em algorithm

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SCANDINAVIAN ACTUARIAL JOURNAL 2024年第6期2024卷 533-560页

作者： Ahmad, Jamaal Bladt, Mogens Univ Copenhagen Dept Math Sci Copenhagen Denmark Univ Copenhagen Dept Math Sci Univ Pk 5 DK-2100 Copenhagen Denmark

In this paper, we consider statistical estimation of time-inhomogeneous aggregate Markov models. Unaggregated models, which corresponds to Markov chains, are commonly used in multi-state life insurance to model the biometric states of an insured. By aggregating microstates to each biometric state, we are able to model dependencies between transitions of the biometric states as well as the distribution of occupancy in these. This allows for non-Markovian modelling in general. Since only paths of the macrostates are observed, we develop an expectation-maximisation (em) algorithm to obtain maximum likelihood estimates of transition intensities on the micro level. Special attention is given to a semi-Markovian case, known as the reset property, which leads to simplified estimation procedures where em algorithms for inhomogeneous phase-type distributions can be used as building blocks. We provide a numerical example of the latter in combination with piecewise constant transition rates in a three-state disability model with data simulated from a time-inhomogeneous semi-Markov model. Comparisons of our fits with more classic GLM-based fits as well as true and empirical distributions are provided to relate our model to existing models and their tools.

关键词： Phase-type distributions Parametric inference em algorithm Multi-state life insurance Semi-Markovianity C46 C63 G22

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Clustering of longitudinal curves via a penalized method and em algorithm

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COMPUTATIONAL STATISTICS 2024年第3期39卷 1485-1512页

作者： Wang, Xin San Diego State Univ Dept Math & Stat 5500 Campanile San Diego CA 92182 USA

In this article, a new method is proposed for clustering longitudinal curves. In the proposed method, clusters of mean functions are identified through a weighted concave pairwise fusion method. The em algorithm and the alternating direction method of multipliers algorithm are combined to estimate the group structure, mean functions and principal components simultaneously. The proposed method also allows to incorporate the prior neighborhood information to have more meaningful groups by adding pairwise weights in the pairwise penalties. In the simulation study, the performance of the proposed method is compared to some existing clustering methods in terms of the accuracy for estimating the number of subgroups and mean functions. The results suggest that ignoring the covariance structure will have a great effect on the performance of estimating the number of groups and estimating accuracy. The effect of including pairwise weights is also explored in a spatial lattice setting to take into consideration of the spatial information. The results show that incorporating spatial weights will improve the performance. A real example is used to illustrate the proposed method.

关键词： ADMM algorithm B-spline regression Clustering em algorithm Functional principal component analysis Penalty functions

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Communication-efficient distributed em algorithm

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STATISTICAL PAPERS 2024年第9期65卷 5575-5592页

作者： Liu, Xirui Wu, Mixia Xu, Liwen Beijing Univ Technol Sch Math Stat & Mech 100 Pingleyuan Beijing 100124 Peoples R China North China Univ Technol Coll Sci 5 Jinyuanzhuang Rd Beijing 100144 Peoples R China

The Expectation Maximization (em) algorithm is widely used in latent variable model inference. However, when data are distributed across various locations, directly applying the em algorithm can often be impractical due to communication expenses and privacy considerations. To address these challenges, a communication-efficient distributed em algorithm is proposed. Under mild conditions, the proposed estimator achieves the same mean squared error bound as the centralized estimator. Furthermore, the proposed method requires only one extra round of communication compared to the Average estimator. Numerical simulations and a real data example demonstrate that the proposed estimator significantly outperforms the Average estimator in terms of mean squared errors.

关键词： em algorithm Distributed inference Communication-efficient Latent variable models

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Experimental Verification of PCH-em algorithm for Characterizing DSERN Image Sensors

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IEEE JOURNAL OF THE ELECTRON DEVICES SOCIETY 2023年 11卷 376-384页

作者： Hendrickson, Aaron J. Haefner, David P. Shade, Nicholas R. Fossum, Eric R. US Navy NAWCAD DAiTA Grp Patuxent River MD 20670 USA US Army C5ISR Ctr Ft Belvoir VA 22060 USA Dartmouth Coll Thayer Sch Engn Dartmouth Hanover NH 03755 USA

The Photon Counting Histogram Expectation Maximization (PCH-em) algorithm has recently been reported as a candidate method for the characterization of Deep Sub-Electron Read Noise (DSERN) image sensors. This work describes a comprehensive demonstration of the PCH-em algorithm applied to a DSERN capable quanta image sensor. The results show that PCH-em is able to characterize DSERN pixels for a large span of quanta exposure and read noise values. The per-pixel characterization results of the sensor are combined with the proposed Photon Counting Distribution (PCD) model to demonstrate the ability of PCH-em to predict the ensemble distribution of the device. The agreement between experimental observations and model predictions demonstrates both the applicability of the PCD model in the DSERN regime as well as the ability of the PCH-em algorithm to accurately estimate the underlying model parameters.

关键词： Conversion gain DSERN em algorithm PCH PCH-em photon counting QIS quanta exposure read noise

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A Robust and Flexible em algorithm for Mixtures of Elliptical Distributions with Missing Data

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2023年 71卷 1669-1682页

作者： Mouret, Florian Hippert-Ferrer, Alexandre Pascal, Frederic Tourneret, Jean-Yves TerraNIS SAS F-31520 Ramonville St Agne France Univ Toulouse IRIT INP ENSEEIHT TeSA F-31000 Toulouse France Univ Paris Saclay CNRS CentraleSupelec Lab Signaux & Syst F-91190 Gif Sur Yvette France Univ Gustave Eiffel IGN ENSG LASTIG F-94165 St Mande France

This article tackles the problem of missing data imputation for noisy and non-Gaussian data. A classical imputation method, the Expectation Maximization (em) algorithm for Gaussian mixture models, has shown interesting properties when compared to other popular approaches such as those based on k-nearest neighbors or on multiple imputations by chained equations. However, Gaussian mixture models are known to be non-robust to heterogeneous data, which can lead to poor estimation performance when the data is contaminated by outliers or have non-Gaussian distributions. To overcome this issue, a new em algorithm is investigated for mixtures of elliptical distributions with the property of handling potential missing data. This paper shows that this problem reduces to the estimation of a mixture of angular Gaussian distributions under generic assumptions (i.e., each sample is drawn from a mixture of elliptical distributions, which is possibly different for one sample to another). In that case, the complete-data likelihood associated with mixtures of elliptical distributions is well adapted to the em framework with missing data thanks to its conditional distribution, which is shown to be a multivariate t-distribution. Experimental results on synthetic data demonstrate that the proposed algorithm is robust to outliers and can be used with non-Gaussian data. Furthermore, experiments conducted on real-world datasets show that this algorithm is very competitive when compared to other classical imputation methods.

关键词： Signal processing algorithms Clustering algorithms Finite element analysis Estimation Generators Classification algorithms Task analysis Angular Gaussian distributions em algorithm elliptical distributions imputation missing data mixture models

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