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, 2011 The Expectation-Maximization (EM) algorithm is a broadly applicable approach to the iterative computation of maximum likelihood (ML) estimates, useful in a variety of incomplete-data problems.
Krishnan, Thriyambakam +2 more
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Implementation of EM algorithm based on non-precise observations [PDF]
Journal of Mahani Mathematical Research, 2023 The EM algorithm is a powerful tool and generic useful device in a variety of problems for maximum likelihood estimation with incomplete data which usually appears in practice.
Abbas Parchami
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Developing a Strategy for Buying and Selling Stocks Based on Semi-Parametric Markov Switching Time Series Models [PDF]
Iranian Journal of Finance, 2021 The modeling of strategies for buying and selling in Stock Market Investment has been the object of numerous advances and uses in economic studies, both theoretically and empirically.
Hossein Naderi +3 more
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Prediction of outstanding IBNR liabilities using delay probability [PDF]
Mathematics and Modeling in Finance, 2021 An important question in non life insurance research is the estimation of number of future payments and corresponding amount of them. A loss reserve is the money set aside by insurance companies to pay policyholders claims on their policies.
Fatemeh Atatalab +1 more
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ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023 ICASSP Conference, 4 pages, 8 ...
Houdouin, Pierre +2 more
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A Legacy of EM Algorithms [PDF]
International Statistical Review, 2022 SummaryNan Laird has an enormous and growing impact on computational statistics. Her paper with Dempster and Rubin on the expectation‐maximisation (EM) algorithm is the second most cited paper in statistics. Her papers and book on longitudinal modelling are nearly as impressive. In this brief survey, we revisit the derivation of some of her most useful
Kenneth Lange, Hua Zhou
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Risks, 2020 This article presents the Poisson-Inverse Gamma regression model with varying dispersion for approximating heavy-tailed and overdispersed claim counts. Our main contribution is that we develop an Expectation-Maximization (EM) type algorithm for maximum ...
George Tzougas
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Weakly Semi Supervised learning based Mixture Model With Two-Level Constraints
Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2021 We propose a new weakly supervised approach for classification and clustering based on mixture models. Our approach integrates multi-level pairwise group and class constraints between samples to learn the underlying group structure of the data and ...
Adama Nouboukpo, Mohand Saïd Allili
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SQUAREM: An R Package for Off-the-Shelf Acceleration of EM, MM and Other EM-Like Monotone Algorithms
Journal of Statistical Software, 2020 We discuss the R package SQUAREM for accelerating iterative algorithms which exhibit slow, monotone convergence. These include the well-known expectation-maximization algorithm, majorize-minimize (MM), and other EM-like algorithms such as expectation ...
Yu Du, Ravi Varadhan
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Modeling Insurance Claims Distribution through Combining Generalized Hyperbolic Skew-t Distribution with Extreme Value Theory [PDF]
تحقیقات مالی, 2016 This paper examines whether combining Generalized Hyperbolic Skew-t distribution, recently introduced in the field of insurance, and Extreme Value Theory (EVT) could result in a modeling of loss function that could model central value as well as extreme ...
Saeed Bajalan +2 more
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