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Communications in Statistics - Theory and Methods, 2020
The models constituting a multiple model will correspond to d treatments of a base design. When we have individual additive models Y(l)=X0β(l)+∑i=1wXiZi(l),l=1,…,d, with Z1(l),…,Zw(l), independent,...
Antunes, Patrícia +3 more
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The models constituting a multiple model will correspond to d treatments of a base design. When we have individual additive models Y(l)=X0β(l)+∑i=1wXiZi(l),l=1,…,d, with Z1(l),…,Zw(l), independent,...
Antunes, Patrícia +3 more
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IEEE Transactions on Neural Networks and Learning Systems, 2021
Sparse additive models have been successfully applied to high-dimensional data analysis due to the flexibility and interpretability of their representation. However, the existing methods are often formulated using the least-squares loss with learning the conditional mean, which is sensitive to data with the non-Gaussian noises, e.g., skewed noise ...
Hong Chen +4 more
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Sparse additive models have been successfully applied to high-dimensional data analysis due to the flexibility and interpretability of their representation. However, the existing methods are often formulated using the least-squares loss with learning the conditional mean, which is sensitive to data with the non-Gaussian noises, e.g., skewed noise ...
Hong Chen +4 more
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Journal of the American Statistical Association, 1989
Abstract Additive isotonic models generalize linear models by replacing lines with isotonic (nondecreasing) transformations. Fitted transformations of several explanatory variables are added together and then transformed by a known function to yield fitted values of the response variable.
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Abstract Additive isotonic models generalize linear models by replacing lines with isotonic (nondecreasing) transformations. Fitted transformations of several explanatory variables are added together and then transformed by a known function to yield fitted values of the response variable.
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Additive models: Extensions and related models [PDF]
, 2012 We give an overview over smooth back tting type estimators in additive models. Moreover we illustrate their wide applicability in models closely related to additive models such as nonparametric regression with dependent error variables where the errors can be transformed to white noise by a linear transformation, nonparametric regression with ...
Mammen, Enno +2 more
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Additive Hazard Regression Models
1997In the last two chapters, we examined regression models for survival data based on a proportional hazards model. In this model, the effect of the covariates was to act multiplicatively on some unknown baseline hazard rate. Covariates which do not act on the baseline hazard rate in this fashion were modeled either by the inclusion of a time-dependent ...
John P. Klein, Melvin L. Moeschberger
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Statistical Methods & Applications, 2016
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Umberto Amato +2 more
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Umberto Amato +2 more
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