Results 11 to 20 of about 6,409,464 (276)
Model Selection in Threshold Models [PDF]
Journal of Time Series Analysis, 2001 This paper considers information criteria as model evaluation tools for nonlinear threshold models. Results concerning the consistency of information criteria in selecting the lag order of linear autoregressive models are extended to nonlinear autoregressive threshold models.
Kapetanios, George
openaire +5 more sources
Bayesian outcome selection modeling
Stat, 2023 In psychiatric and social epidemiology studies, it is common to measure multiple different outcomes using a comprehensive battery of tests thought to be related to an underlying construct of interest. In the research that motivates our work, researchers wanted to assess the impact of in utero alcohol exposure on child cognition and neuropsychological ...
Khue‐Dung Dang +5 more
openaire +5 more sources
Journal of Classification, 2020 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
P. Giudici, E. Raffinetti
openaire +2 more sources
Evaluating Automatic Model Selection [PDF]
Journal of Time Series Econometrics, 2011 We outline a range of criteria for evaluating model selection approaches that have been used in the literature. Focusing on three key criteria, we evaluate automatically selecting the relevant variables in an econometric model from a large candidate set.
Jennifer Castle +2 more
openaire +5 more sources
[1992] Proceedings. 11th IAPR International Conference on Pattern Recognition, 2003 Proposes an efficient architecture for selective image modeling. The authors give an example in which models of different scale are reconstructed in parallel. It is shown that this redundant representation can effectively be pruned using the criterion of minimum description length.
Solina, Franc, Leonardis, Aleš
openaire +2 more sources
Model selection for amplitude analysis [PDF]
, 2015 Model complexity in amplitude analyses is often a priori under-constrained since the underlying theory permits a large number of possible amplitudes to contribute to most physical processes.
Guegan, Baptiste +3 more
core +2 more sources
Bootstrap for neural model selection [PDF]
, 2002 Bootstrap techniques (also called resampling computation techniques) have introduced new advances in modeling and model evaluation. Using resampling methods to construct a series of new samples which are based on the original data set, allows to estimate
Cottrell, Marie +2 more
core +3 more sources
Journal of the Royal Statistical Society Series B: Statistical Methodology, 1995 SUMMARY We consider the problem of selecting one model from a large class of plausible models. A predictive Bayesian viewpoint is advocated to avoid the specification of prior probabilities for the candidate models and the detailed interpretation of the parameters in each model.
Laud, Purushottam W., Ibrahim, Joseph G.
openaire +2 more sources
Adaptive Covariance Estimation with model selection [PDF]
, 2012 We provide in this paper a fully adaptive penalized procedure to select a covariance among a collection of models observing i.i.d replications of the process at fixed observation points. For this we generalize previous results of Bigot and al.
A. G. Journel +12 more
core +4 more sources
Analysis of Testing-Based Forward Model Selection [PDF]
, 2020 This paper introduces and analyzes a procedure called Testing-based forward model selection (TBFMS) in linear regression problems. This procedure inductively selects covariates that add predictive power into a working statistical model before estimating ...
Kozbur, Damian
core +1 more source