Stochastic Expectation Maximization Algorithm for Linear Mixed-Effects Model with Interactions in the Presence of Incomplete Data [PDF]
Entropy, 2023 The purpose of this paper is to propose a new algorithm based on stochastic expectation maximization (SEM) to deal with the problem of unobserved values when multiple interactions in a linear mixed-effects model (LMEM) are present.
Alandra Zakkour +2 more
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Spectrum adapted expectation-maximization algorithm for high-throughput peak shift analysis [PDF]
Science and Technology of Advanced Materials, 2019 We introduce a spectrum-adapted expectation-maximization (EM) algorithm for high-throughput analysis of a large number of spectral datasets by considering the weight of the intensity corresponding to the measurement energy steps.
Tarojiro Matsumura +4 more
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RPEM: Randomized Monte Carlo parametric expectation maximization algorithm [PDF]
CPT: Pharmacometrics & Systems PharmacologyInspired from quantum Monte Carlo, by sampling discrete and continuous variables at the same time using the Metropolis–Hastings algorithm, we present a novel, fast, and accurate high performance Monte Carlo Parametric Expectation Maximization (MCPEM ...
Rong Chen +9 more
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A quantile variant of the expectation–maximization algorithm and its application to parameter estimation with interval data [PDF]
Journal of Algorithms & Computational Technology, 2018 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.
Chanseok Park
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Dynamic Expectation Maximization Algorithm for Estimation of Linear Systems with Colored Noise [PDF]
Entropy, 2021 The free energy principle from neuroscience has recently gained traction as one of the most prominent brain theories that can emulate the brain’s perception and action in a bio-inspired manner.
Ajith Anil Meera, Martijn Wisse
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Adaptive Cubature Kalman Filter Based on the Expectation-Maximization Algorithm [PDF]
IEEE Access, 2019 A cubature Kalman filter is considered to be one of the most useful methods for nonlinear systems. However, when the statistical characteristics of noise are unknown, the estimation accuracy is degraded. Therefore, an adaptive square-root cubature Kalman
Weidong Zhou, Lu Liu
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Multi-Sensor Recursive EM Algorithm for Robust Identification of ARX Models [PDF]
SensorsA robust multi-sensor recursive Expectation-Maximization (RMSREM) algorithm is proposed in this paper for autoregressive eXogenous (ARX) models, addressing the challenges of heavy-tailed noise, as well as the difficulty in simultaneously processing multi-
Xin Chen, Jiale Li
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Efficient estimation of Markov-switching model with application in stock price classification [PDF]
Mathematics and Modeling in Finance, 2021 In this paper, we discuss the calibration of the geometric Brownian motion model equipped with Markov-switching factor. Since the motivation for this research comes from a recent stream of literature in stock economics, we propose an efficient estimation
Farshid Mehrdoust +2 more
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Mathematics, 2023 Extensive research has been conducted on models that utilize the Kumaraswamy distribution to describe continuous variables with bounded support. In this study, we examine the trapezoidal Kumaraswamy model.
Jorge Figueroa-Zúñiga +4 more
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Applied Sciences, 2022 Only a specific location can make sensor data useful. The paper presents an simplify belief propagation and variation expectation maximization (SBPVEM) algorithm to achieve node localization by cooperating with another target node while lowering ...
Xueying Wang +5 more
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