Results 1 to 10 of about 1,286,114 (315)
On a mixture autoregressive model
Journal of the Royal Statistical Society Series B: Statistical Methodology, 2000 Summary We generalize the Gaussian mixture transition distribution (GMTD) model introduced by Le and co-workers to the mixture autoregressive (MAR) model for the modelling of non-linear time series. The models consist of a mixture of K stationary or non-stationary AR components. The advantages of the MAR model over the GMTD model include
Wong, CS, Li, WK
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Entropy, 2021 The degree distribution has attracted considerable attention from network scientists in the last few decades to have knowledge of the topological structure of networks. It is widely acknowledged that many real networks have power-law degree distributions.
Hohyun Jung, Frederick Kin Hing Phoa
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Clustering Longitudinal Data Using R: A Monte Carlo Study
Methodology, 2022 The analysis of change within subjects over time is an ever more important research topic. Besides modelling the individual trajectories, a related aim is to identify clusters of subjects within these trajectories.
Peter Verboon, Ron Pat-El
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Modeling Recovery Rates of Small- and Medium-Sized Entities in the US
Mathematics, 2020 A sound statistical model for recovery rates is required for various applications in quantitative risk management, with the computation of capital requirements for loan portfolios as one important example.
Aleksey Min +3 more
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A family of mixture models for biclustering [PDF]
Statistical Analysis and Data Mining: The ASA Data Science Journal, 2021 AbstractBiclustering is used for simultaneous clustering of the observations and variables when there is no group structure known a priori. It is being increasingly used in bioinformatics, text analytics, and so on. Previously, biclustering has been introduced in a model‐based clustering framework by utilizing a structure similar to a mixture of factor
Wangshu Tu, Sanjeena Subedi
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Graph Laplacian Mixture Model [PDF]
IEEE Transactions on Signal and Information Processing over Networks, 2020 Graph learning methods have recently been receiving increasing interest as means to infer structure in datasets. Most of the recent approaches focus on different relationships between a graph and data sample distributions, mostly in settings where all available data relate to the same graph.
Hermina Petric Maretic, Pascal Frossard
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CFD Comparison of multiphase models in the pool boiling state
Curved and Layered Structures, 2022 With the development of simulation technology and programs, it became necessary to study the models that control equations’ solutions and influence the results.
Enjadat Suleiman MJ.
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Entropy, 2021 Predicting the void fraction of a two-phase flow outside of tubes is essential to evaluate the thermohydraulic behaviour in steam generators. Indeed, it determines two-phase mixture properties and affects two-phase mixture velocity, which enable ...
Claire Dubot +5 more
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Mathematics, 2021 In this study, thermal performance and flow characteristics of a shell and tube heat exchanger equipped with various baffle angles were studied. The heat exchanger was operated with distilled water, and a hybrid nanofluid at three concentrations of 0.04%
Mashhour A. Alazwari +1 more
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A Variational Autoencoder Mixture Model for Online Behavior Recommendation
IEEE Access, 2020 Online behavior recommendation is an attractive research topic related to social media mining. This topic focuses on suggesting suitable behaviors for users in online platforms, including music listening, video watching, e-commerce, to name but a few to ...
Minh-Duc Nguyen, Yoon-Sik Cho
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