Results 31 to 40 of about 6,487,763 (293)
ComputationPredicting corporate bankruptcy is a key task in financial risk management, and selecting a machine learning model with superior generalization performance is crucial for prediction accuracy.
Vlad Teodorescu +1 more
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Geosciences, 2019 Seabed sediment predictions at regional and national scales in Australia are mainly based on bathymetry-related variables due to the lack of backscatter-derived data. In this study, we applied random forests (RFs), hybrid methods of RF and geostatistics,
Jin Li +3 more
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Model selection and local geometry
, 2019 We consider problems in model selection caused by the geometry of models close to their points of intersection. In some cases---including common classes of causal or graphical models, as well as time series models---distinct models may nevertheless have ...
Evans, Robin J.
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An Introduction to Model Selection
Journal of Mathematical Psychology, 2000 This paper is an introduction to model selection intended for nonspecialists who have knowledge of the statistical concepts covered in a typical first (occasionally second) statistics course. The intention is to explain the ideas that generate frequentist methodology for model selection, for example the Akaike information criterion, bootstrap criteria,
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Induction as model selection [PDF]
Proceedings of the National Academy of Sciences, 2008 Overview of hierarchical Bayesian approach to learning structural form proposed by Kemp and Tenenbaum (3), using examples of similarities among a set of animals. (A) The data at the bottom, in the form of a feature vector for each animal, can potentially be produced by alternative forms (ring, partition, tree, order, hierarchy) that can take on many ...
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Maxisets for Model Selection [PDF]
Constructive Approximation, 2009 We address the statistical issue of determining the maximal spaces (maxisets) where model selection procedures attain a given rate of convergence. By considering first general dictionaries, then orthonormal bases, we characterize these maxisets in terms of approximation spaces.
Autin, Florent +3 more
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Model Order Selection for Collision Multiplicity Estimation [PDF]
, 2012 The collision multiplicity (CM) is the number of users involved in a collision. The CM estimation is an essential step in multi-packet reception (MPR) techniques and in collision resolution (CR) methods.
Escrig, Benoît
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Selecting Models with Judgment [PDF]
SSRN Electronic Journal, 2018 A statistical decision rule incorporating judgment does not perform worse than a judgmental decision with a given probability. Under model misspecification, this probability is unknown. The best model is the least misspecified, as it is the one whose probability of underperforming the judgmental decision is closest to the chosen probability.
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Model Selection and Post Selection to Improve the Estimation of the ARCH Model
Journal of Risk and Financial Management, 2022 The Autoregressive Conditionally Heteroscedastic (ARCH) model is useful for handling volatilities in economical time series phenomena that ARIMA models are unable to handle. The ARCH model has been adopted in many applications that contain time series data such as financial market prices, options, commodity prices and the oil industry.
Marwan Al-Momani, Abdaljbbar B. A. Dawod
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A Large-Scale Empirical Study of Aligned Time Series Forecasting
IEEE AccessAutomated Machine Learning (AutoML) tools for time series forecasting represent a frontier in both academic and industrial research, addressing the need for efficient, accurate predictions in various domains.
Polina Pilyugina +6 more
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