The MIAmaxent R package: Variable transformation and model selection for species distribution models
Sign Up to like & getrecommendations! Published in 2019 at "Ecology and Evolution"
DOI: 10.1002/ece3.5654
Abstract: Abstract The widely used “Maxent” software for modeling species distributions from presence‐only data (Phillips et al., Ecological Modelling, 190, 2006, 231) tends to produce models with high‐predictive performance but low‐ecological interpretability, and implications of Maxent's… read more here.
Keywords: selection; variable transformation; model; model selection ... See more keywords
Spatiotemporal multivariate mixture models for Bayesian model selection in disease mapping.
Sign Up to like & getrecommendations! Published in 2017 at "Environmetrics"
DOI: 10.1002/env.2465
Abstract: It is often the case that researchers wish to simultaneously explore the behavior of and estimate overall risk for multiple, related diseases with varying rarity while accounting for potential spatial and/or temporal correlation. In this… read more here.
Keywords: model; model selection; mixture models; spatiotemporal multivariate ... See more keywords
A model selection framework to quantify microvascular liver function in gadoxetate‐enhanced MRI: Application to healthy liver, diseased tissue, and hepatocellular carcinoma
Sign Up to like & getrecommendations! Published in 2021 at "Magnetic Resonance in Medicine"
DOI: 10.1002/mrm.28798
Abstract: We introduce a novel, generalized tracer kinetic model selection framework to quantify microvascular characteristics of liver and tumor tissue in gadoxetate‐enhanced dynamic contrast‐enhanced MRI (DCE‐MRI). read more here.
Keywords: framework quantify; gadoxetate enhanced; selection framework; liver ... See more keywords
A model selection approach to jointly testing for structural breaks and cointegration with application to the Eurocurrency interest rates market
Sign Up to like & getrecommendations! Published in 2020 at "Empirical Economics"
DOI: 10.1007/s00181-020-01916-1
Abstract: All tests involving both structural breaks and cointegration are parametric. As a complement to the classical hypothesis testing for empirical researchers, we suggest the use of a one-step model selection approach to simultaneously specifying lag… read more here.
Keywords: structural breaks; cointegration; model selection; breaks cointegration ... See more keywords
A new method for estimation and model selection:$$\rho $$ρ-estimation
Sign Up to like & getrecommendations! Published in 2017 at "Inventiones mathematicae"
DOI: 10.1007/s00222-016-0673-5
Abstract: The aim of this paper is to present a new estimation procedure that can be applied in various statistical frameworks including density and regression and which leads to both robust and optimal (or nearly optimal)… read more here.
Keywords: estimation; estimation model; new method; method estimation ... See more keywords
A Variational Approximations-DIC Rubric for Parameter Estimation and Mixture Model Selection Within a Family Setting
Sign Up to like & getrecommendations! Published in 2021 at "Journal of Classification"
DOI: 10.1007/s00357-019-09351-3
Abstract: Mixture model-based clustering has become an increasingly popular data analysis technique since its introduction over fifty years ago, and is now commonly utilized within a family setting. Families of mixture models arise when the component… read more here.
Keywords: parameter estimation; family; model; model selection ... See more keywords
Investigating the effect of complexity on groundwater flow modeling uncertainty
Sign Up to like & getrecommendations! Published in 2017 at "Stochastic Environmental Research and Risk Assessment"
DOI: 10.1007/s00477-017-1436-6
Abstract: Considering complexity in groundwater modeling can aid in selecting an optimal model, and can avoid over parameterization, model uncertainty, and misleading conclusions. This study was designed to determine the uncertainty arising from model complexity, and… read more here.
Keywords: uncertainty; complexity; model; model selection ... See more keywords
Model selection and application to high-dimensional count data clustering
Sign Up to like & getrecommendations! Published in 2018 at "Applied Intelligence"
DOI: 10.1007/s10489-018-1333-9
Abstract: EDCM, the Exponential-family approximation to the Dirichlet Compound Multinomial (DCM), proposed by Elkan (2006), is an efficient statistical model for high-dimensional and sparse count data. EDCM models take into account the burstiness phenomenon correctly while… read more here.
Keywords: high dimensional; model; model selection; count data ... See more keywords
Automated White Matter Hyperintensity Segmentation Using Bayesian Model Selection: Assessment and Correlations with Cognitive Change
Sign Up to like & getrecommendations! Published in 2020 at "Neuroinformatics"
DOI: 10.1007/s12021-019-09439-6
Abstract: Accurate, automated white matter hyperintensity (WMH) segmentations are needed for large-scale studies to understand contributions of WMH to neurological diseases. We evaluated Bayesian Model Selection (BaMoS), a hierarchical fully-unsupervised model selection framework for WMH segmentation.… read more here.
Keywords: model selection; segmentation; cognitive change;
Ensemble wind speed forecasting with multi-objective Archimedes optimization algorithm and sub-model selection
Sign Up to like & getrecommendations! Published in 2021 at "Applied Energy"
DOI: 10.1016/j.apenergy.2021.117449
Abstract: Abstract Wind energy is becoming increasingly competitive and promising for renewable energy profiles. Accurate and reliable wind speed prediction is crucial for the effective exploitation of wind energy. However, previous studies have generally ignored the… read more here.
Keywords: model selection; sub model; wind; multi objective ... See more keywords
Model selection in spectroscopic ellipsometry data analysis: Combining an information criteria approach with screening sensitivity analysis
Sign Up to like & getrecommendations! Published in 2017 at "Applied Surface Science"
DOI: 10.1016/j.apsusc.2016.09.139
Abstract: Abstract In the field of optical metrology, the selection of the best model to fit experimental data is absolutely nontrivial problem. In practice, this is a very subjective and formidable task which highly depends on… read more here.
Keywords: analysis; model; model selection; information criteria ... See more keywords