Results 31 to 40 of about 9,497,746 (229)
, 2018 The expectation-maximization (EM) algorithm is a powerful computational technique for finding the maximum likelihood estimates for parametric models when the data are not fully observed.
Chanseok Park +8 more
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Unsupervised cryo-EM data clustering through adaptively constrained K-means algorithm
, 2016 In single-particle cryo-electron microscopy (cryo-EM), K-means clustering algorithm is widely used in unsupervised 2D classification of projection images of biological macromolecules.
Mao, Youdong +3 more
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Semi-Parametric Joint Modeling of Survival and Longitudinal Data: The R Package JSM
Journal of Statistical Software, 2020 This paper is devoted to the R package JSM which performs joint statistical modeling of survival and longitudinal data. In biomedical studies it has been increasingly common to collect both baseline and longitudinal covariates along with a possibly ...
Cong Xu +2 more
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, 1950 This report reviews the Expectation Maximization EM algorithm and applies it to the data segmentation problem yielding the Expectation Maximization Segmentation EMS algorithm The EMS algorithm requires batch processing of the data and can be applied to ...
Kramer, Robert
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Color image segmentation using a self-initializing EM algorithm [PDF]
, 2006 This paper presents a new method based on the Expectation-Maximization (EM) algorithm that we apply for color image segmentation. Since this algorithm partitions the data based on an initial set of mixtures, the color segmentation provided by the EM ...
Ilea, Dana E., Whelan, Paul F.
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Data Science JournalSophisticated data-intensive approaches have been widely applied in addressing air pollution problems, with applications ranging from remote sensing quantification of ground-level concentrations of atmospheric pollutants to associating particulate matter
Kassim Mwitondi, Hugo Wai Leung Mak
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Distributed localization of a RF target in NLOS environments
, 2014 We propose a novel distributed expectation maximization (EM) method for non-cooperative RF device localization using a wireless sensor network. We consider the scenario where few or no sensors receive line-of-sight signals from the target. In the case of
Leng, Mei +4 more
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The heavy-tailed chi-square model: properties, estimation and application to wind speed data
AIMS MathematicsIn this article, we introduced an extension of the chi-square distribution by employing a slash-type methodology that enhanced the weight of the right tail, thereby producing a heavy-tailed distribution.
Eliseo Martínez +4 more
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Comparison of the Average Kappa Coefficients of Two Binary Diagnostic Tests with Missing Data
Mathematics, 2021 The average kappa coefficient of a binary diagnostic test is a parameter that measures the average beyond-chance agreement between the diagnostic test and the gold standard.
José Antonio Roldán-Nofuentes +1 more
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Latent Class Probabilistic Latent Feature Analysis of Three-Way Three-Mode Binary Data
Journal of Statistical Software, 2018 The analysis of binary three-way data (i.e., persons who indicate which attributes apply to each of a set of objects) may be of interest in several substantive domains as sensory profiling, marketing research or personality assessment.
Michel Meulders, Philippe De Bruecker
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