Results 11 to 20 of about 316,835 (263)

Mixture model averaging for clustering [PDF]

Advances in Data Analysis and Classification, 2014
In mixture model-based clustering applications, it is common to fit several models from a family and report clustering results from only the `best' one. In such circumstances, selection of this best model is achieved using a model selection criterion, most often the Bayesian information criterion.
Yuhong Wei, Paul D. McNicholas
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Confidence Distributions for FIC Scores

Econometrics, 2020
When using the Focused Information Criterion (FIC) for assessing and ranking candidate models with respect to how well they do for a given estimation task, it is customary to produce a so-called FIC plot. This plot has the different point estimates along
Céline Cunen, Nils Lid Hjort
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RETRACTED: Deep Fractional Max Pooling Neural Network for COVID-19 Recognition

Frontiers in Public Health, 2021
Aim: Coronavirus disease 2019 (COVID-19) is a form of disease triggered by a new strain of coronavirus. This paper proposes a novel model termed “deep fractional max pooling neural network (DFMPNN)” to diagnose COVID-19 more efficiently.Methods: This 12 ...
Shui-Hua Wang, Suresh Chandra Satapathy, Donovan Anderson, Shi-Xin Chen, Yu-Dong Zhang   +4 more
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Recovering Crossed Random Effects in Mixed-Effects Models Using Model Averaging

Methodology, 2022
Random effects contain crucial information to understand the variability of the processes under study in mixed-effects models with crossed random effects (MEMs-CR).
José Ángel Martínez-Huertas, Ricardo Olmos   +1 more
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Averaging and Stacking Partial Least Squares Regression Models to Predict the Chemical Compositions and the Nutritive Values of Forages from Spectral Near Infrared Data

Applied Sciences, 2022
Partial least square regression (PLSR) is a reference statistical model in chemometrics. In agronomy, it is used to predict components (response variables y) of chemical composition of vegetal materials from spectral near infrared (NIR) data X collected ...
Mathieu Lesnoff, Donato Andueza, Charlène Barotin, Philippe Barre, Laurent Bonnal, Juan Antonio Fernández Pierna, Fabienne Picard, Philippe Vermeulen, Jean-Michel Roger   +8 more
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Systematic Comparison of Hospital-Wide Standard and Model-Based Therapeutic Drug Monitoring of Vancomycin in Adults

Pharmaceutics, 2022
We aimed to evaluate the predictive performance and predicted doses of a single-model approach or several multi-model approaches compared with the standard therapeutic drug monitoring (TDM)-based vancomycin dosing.
Heleen Gastmans, Erwin Dreesen, Sebastian G. Wicha, Nada Dia, Ellen Spreuwers, Annabel Dompas, Karel Allegaert, Stefanie Desmet, Katrien Lagrou, Willy E. Peetermans, Yves Debaveye, Isabel Spriet, Matthias Gijsen   +12 more
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BACE and BMA Variable Selection and Forecasting for UK Money Demand and Inflation with Gretl

Econometrics, 2020
In this paper, we apply Bayesian averaging of classical estimates (BACE) and Bayesian model averaging (BMA) as an automatic modeling procedures for two well-known macroeconometric models: UK demand for narrow money and long-term inflation.
Marcin Błażejowski, Jacek Kwiatkowski, Paweł Kufel   +2 more
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Automating Model Comparison in Factor Graphs

Entropy, 2023
Bayesian state and parameter estimation are automated effectively in a variety of probabilistic programming languages. The process of model comparison on the other hand, which still requires error-prone and time-consuming manual derivations, is often ...
Bart van Erp, Wouter W. L. Nuijten, Thijs van de Laar, Bert de Vries   +3 more
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The sustainability of Serbia's external position: The impact of fiscal adjustment and external shocks [PDF]

Ekonomski Anali, 2015
This paper studies the impact of fiscal policy and external shocks on the sustainability of Serbia’s external position. The key determinants of Serbia's current account balance are identified using model averaging techniques and are compared ...
Zildžović Emir
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Model Averaging in Economics [PDF]

SSRN Electronic Journal, 2011
Model uncertainty remains a challenge to applied researchers in economics. When many competing models are available for estimation and without enough guidance from theory, model averaging represents an alternative to model selection. Despite model averaging approaches have been present in statistics for many years, only over the recent decades are ...
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