Results 41 to 50 of about 25,321,415 (248)
Regularization Methods for Additive Models [PDF]
, 2003 This paper tackles the problem of model complexity in the context of additive models. Several methods have been proposed to estimate smoothing parameters, as well as to perform variable selection. However, these procedures are inefficient or computationally expensive in high dimension.
Avalos, Marta +2 more
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High-dimensional Structured Additive Regression Models: Bayesian Regularisation, Smoothing and Predictive Performance [PDF]
, 2009 Data structures in modern applications frequently combine the necessity of flexible regression techniques such as nonlinear and spatial effects with high-dimensional covariate vectors. While estimation of the former is typically achieved by supplementing
Konrath, Susanne +2 more
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Optimal design for additive partially nonlinear models [PDF]
, 2010 We develop optimal design theory for additive partially nonlinear regression models, showing that Bayesian and standardized maximin D-optimal designs can be found as the products of the corresponding optimal designs in one dimension.
D. C. Woods +9 more
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additive: Bindings for Additive TidyModels [PDF]
, 2022 Fit Generalized Additive Models (GAM) using 'mgcv' with 'parsnip'/'tidymodels' via 'additive' . 'tidymodels' is a collection of packages for machine learning; see Kuhn and Wickham (2020) ). The technical details of 'mgcv' are described in Wood (2017) .To
Badr, Hamada S.
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Stable and Efficient Multiple Smoothing Parameter Estimation for Generalized Additive Models
, 2004 Representation of generalized additive models (GAM's) using penalized regression splines allows GAM's to be employed in a straightforward manner using penalized regression methods.
S. Wood
semanticscholar +1 more source
Normal-Mixture-of-Inverse-Gamma Priors for Bayesian Regularization and Model Selection in Structured Additive Regression Models [PDF]
, 2010 In regression models with many potential predictors, choosing an appropriate subset of covariates and their interactions at the same time as determining whether linear or more flexible functional forms are required is a challenging and important task. We
Scheipl, Fabian
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On concurvity in nonlinear and nonparametric regression models
Statistica, 2014 When data are affected by multicollinearity in the linear regression framework, then concurvity will be present in fitting a generalized additive model (GAM). The term concurvity describes nonlinear dependencies among the predictor variables.
Sonia Amodio +2 more
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Additive and non-additive genetic effects of humoral immune traits in Japanese quail
Journal of Applied Poultry Research, 2022 SUMMARY: In breeding programs, using appropriate models to estimate the variance components with high accuracy is essential. Considering the non-additive genetic effects along with additive effects in evaluation analysis models can be effective in ...
H. Faraji-Arough +3 more
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Вестник Самарского университета: Естественнонаучная серия, 2023 For modeling in time series, models with fractional differences are widely used. The best known model is the ARFIMA (autoregressive fractionally integrated moving average) model.
Dmitriy V. Ivanov
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Flexible semiparametric mixed models [PDF]
, 2005 In linear mixed models the influence of covariates is restricted to a strictly parametric form. With the rise of semi- and nonparametric regression also the mixed model has been expanded to allow for additive predictors.
Reithinger, Florian, Tutz, Gerhard
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