Results 11 to 20 of about 70,549 (302)
Generalized Additive Models for Location Scale and Shape (GAMLSS) in R [PDF]
Journal of Statistical Software, 2007 GAMLSS is a general framework for fitting regression type models where the distribution of the response variable does not have to belong to the exponential family and includes highly skew and kurtotic continuous and discrete distribution.
D. Mikis Stasinopoulos, Robert A. Rigby
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The VGAM Package for Categorical Data Analysis [PDF]
Journal of Statistical Software, 2010 Classical categorical regression models such as the multinomial logit and proportional odds models are shown to be readily handled by the vector generalized linear and additive model (VGLM/VGAM) framework. Additionally, there are natural extensions, such
Thomas W. Yee
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Application of Machine Learning Techniques to High-Dimensional Clinical Data to Forecast Postoperative Complications. [PDF]
PLoS ONE, 2016 OBJECTIVE:To compare performance of risk prediction models for forecasting postoperative sepsis and acute kidney injury. DESIGN:Retrospective single center cohort study of adult surgical patients admitted between 2000 and 2010.
Paul Thottakkara +6 more
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evgam: An R Package for Generalized Additive Extreme Value Models
Journal of Statistical Software, 2022 This article introduces the R package evgam. The package provides functions for fitting extreme value distributions. These include the generalized extreme value and generalized Pareto distributions.
Benjamin D. Youngman
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Mathematics, 2023 Generalized Linear Models (GLMs) are the standard tool used for pricing in the field of automobile insurance. Generalized Additive Models (GAMs) are more complex and computationally intensive but allow taking into account nonlinear effects without the ...
Zuleyka Díaz Martínez +2 more
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Epidemics, 2020 In the United States, new tuberculosis cases are increasingly concentrated within non-native-born populations. We estimated trends and differences in tuberculosis incidence rates for the non-U.S.-born population, at a resolution unobtainable from raw ...
Andrew N. Hill +3 more
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Interpretable Ranking with Generalized Additive Models [PDF]
Proceedings of the 14th ACM International Conference on Web Search and Data Mining, 2021 Interpretability of ranking models is a crucial yet relatively under-examined research area. Recent progress on this area largely focuses on generating post-hoc explanations for existing black-box ranking models. Though promising, such post-hoc methods cannot provide sufficiently accurate explanations in general, which makes them infeasible in many ...
Honglei Zhuang +9 more
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Generalized Additive Models: An Introduction with R
Journal of Statistical Software, 2006 s not available for ...
John Maindonald
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Revista de Saúde Pública, 2014 OBJECTIVE To analyze the association between concentrations of air pollutants and admissions for respiratory causes in children. METHODS Ecological time series study.
Juliana Bottoni de Souza +3 more
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Generalized Sparse Additive Models
Journal of machine learning research : JMLR, 2019 We present a unified framework for estimation and analysis of generalized additive models in high dimensions. The framework defines a large class of penalized regression estimators, encompassing many existing methods. An efficient computational algorithm for this class is presented that easily scales to thousands of observations and features.
Asad Haris, Noah Simon, Ali Shojaie
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