Results 11 to 20 of about 1,959,808 (288)
Frontiers in Nutrition, 2021 This review uses person-centered research and data analysis strategies to discuss the conceptualization and measurement of appetite self-regulation (ASR) phenotypes and trajectories in childhood (from infancy to about ages 6 or 7 years). Research that is
Alan Russell +2 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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A Bayesian latent mixture model approach to assessing performance in stock-flow reasoning
Judgment and Decision Making, 2017 People often perform poorly on stock-flow reasoning tasks, with many (but not all) participants appearing to erroneously match the accumulation of the stock to the inflow – a response pattern attributed to the use of a “correlation heuristic”. Efforts to
Arthur Kary +3 more
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flexCWM: A Flexible Framework for Cluster-Weighted Models
Journal of Statistical Software, 2018 Cluster-weighted models (CWMs) are mixtures of regression models with random covariates. However, besides having recently become rather popular in statistics and data mining, there is still a lack of support for CWMs within the most popular statistical ...
Angelo Mazza +2 more
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Testing for Homogeneity in Mixture Models [PDF]
, 2016 Statistical models of unobserved heterogeneity are typically formalized as mixtures of simple parametric models and interest naturally focuses on testing for homogeneity versus general mixture alternatives.
Gu, Jiaying +2 more
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Local mixture models of exponential families [PDF]
, 2007 Exponential families are the workhorses of parametric modelling theory. One reason for their popularity is their associated inference theory, which is very clean, both from a theoretical and a computational point of view.
Anaya-Izquierdo, Karim, Marriott, Paul
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IEEE Access, 2018 The destructive effects of cyber-attacks demand more proactive security approaches. One such promising approach is the idea of collaborative intrusion detection systems (CIDSs).
Carlos Garcia Cordero +6 more
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Optimal transport for Gaussian mixture models [PDF]
, 2018 We present an optimal mass transport framework on the space of Gaussian mixture models, which are widely used in statistical inference. Our method leads to a natural way to compare, interpolate and average Gaussian mixture models.
Chen, Yongxin +2 more
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Computational aspects of N-mixture models [PDF]
, 2014 The N-mixture model is widely used to estimate the abundance of a population in the presence of unknown detection probability from only a set of counts subject to spatial and temporal replication (Royle, 2004, Biometrics 60,105–115).
+29 more
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How to fit models of recognition memory data using maximum likelihood.
International Journal of Psychological Research, 2010 The aim of this paper is to provide an introductory tutorial to how to fit different models of recognition memory using maximum likelihood estimation. It is in four main parts.
John C. Dunn
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