Results 1 to 10 of about 831,124 (298)
Kernel-Based Information Criterion
Computer and Information Science, 2015 This paper introduces Kernel-based Information Criterion (KIC) for model selection in regression analysis. The novel kernel-based complexity measure in KIC efficiently computes the interdependency between parameters of the model using a variable-wise variance and yields selection of better, more robust regressors.
Somayeh Danafar +2 more
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Selection Criteria for Overlapping Binary Models—A Simulation Study
Mathematics, 2022 This paper deals with the problem of choosing the optimum criterion for selecting the best model out of a set of overlapping binary models. The criteria we studied were the well-known AIC and SBIC, and a third one called C2. Special attention was paid to
Teresa Aparicio, Inmaculada Villanúa
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Журнал інженерних наук, 2022 The article considers the problem of machine learning of a wrist prosthesis control system with a non-invasive biosignal reading system. The task is solved within the framework of information-extreme intelligent data analysis technology, which is based ...
Suprunenko M. K. +2 more
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Economies, 2020 Only unstructured single-path model selection techniques, i.e., Information Criteria, are used by Bounds test of cointegration for model selection.
Waqar Badshah, Mehmet Bulut
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Machine Learning Methods for Inferring the Number of UAV Emitters via Massive MIMO Receive Array
Drones, 2023 To provide important prior knowledge for the direction of arrival (DOA) estimation of UAV emitters in future wireless networks, we present a complete DOA preprocessing system for inferring the number of emitters via a massive multiple-input multiple ...
Yifan Li +8 more
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Yantu gongcheng xuebao, 2023 In order to establish an optimal model for estimating the uniaxial compressive strength (UCS) of rocks as well as its reasonable estimation, a fully Bayesian Gaussian process regression method (fB-GPR) is proposed by combining the Gaussian process ...
SONG Chao , ZHAO Tengyuan , XU Ling
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Nonlinear predictive model selection and model averaging using information criteria
Systems Science & Control Engineering, 2018 This paper is concerned with the model selection and model averaging problems in system identification and data-driven modelling for nonlinear systems. Given a set of data, the objective of model selection is to evaluate a series of candidate models and ...
Yuanlin Gu +2 more
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Modeling crude oil price volatility in Nigeria: using GARCH (1,1), EGARCH (1,1), and GJR-GARCH (1,1) models [PDF]
مجلة العلوم التجارية والبيئيةThis study investigates the performance of various GARCH models for volatility forecasting, focusing on the GARCH (1,1), EGARCH (1,1), and GJR-GARCH (1,1) frameworks, each tested with normal and Student’s t-distributions.
Frederick A. Omoruyi +2 more
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Is your ad hoc model selection strategy affecting your multimodel inference?
Ecosphere, 2020 Ecologists routinely fit complex models with multiple parameters of interest, where hundreds or more competing models are plausible. To limit the number of fitted models, ecologists often define a model selection strategy composed of a series of stages ...
Dana J. Morin +6 more
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Heliyon, 2023 Multivariate Adaptive Regression Splines (MARS) is a useful non-parametric regression analysis method that can be used for model selection in high-dimensional data. Since MARS can identify and model complex, non-linear relationships between the dependent
Meryem Bekar Adiguzel, Mehmet Ali Cengiz
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