Results 31 to 40 of about 280,784 (278)

Generalized Additive Models with Unknown Link Function Including Variable Selection [PDF]

, 2013
The generalized additive model is a well established and strong tool that allows to model smooth effects of predictors on the response. However, if the link function, which is typically chosen as the canonical link, is misspecified, substantial bias is ...
Petry, Sebastian, Tutz, Gerhard
core   +1 more source

Wildfires Impact Assessment on PM Levels Using Generalized Additive Mixed Models

Atmosphere, 2023
Wildfires are relevant sources of PM emissions and can have an important impact on air pollution and human health. In this study, we examine the impact of wildfire PM emissions on the Piemonte (Italy) air quality regional monitoring network using a ...
Gianluca Leone, Giorgio Cattani, Mariacarmela Cusano, Alessandra Gaeta, Guido Pellis, Marina Vitullo, Raffaele Morelli   +6 more
doaj   +1 more source

PEMODELAN PRODUKTIVITAS PADI DENGAN MENGGUNAKAN GENERALIZED ADDITIVE MODELS DI PROVINSI BANTEN

Jurnal Lebesgue, 2020
Tujuan dari penelitian ini adalah untuk melakukan pemodelan produktivitas padi. Sumber data yang digunakan dalam penelitian ini adalah data produktivitas padi, penggunaan pupuk, penggunaan benih, sistem tanam, serangan OPT, dampak perubahan iklim, dan ...
Wahyudi Manurung, Muhammad Fajar, Noviar
doaj   +1 more source

Maximal Associated Regression: A Nonlinear Extension to Least Angle Regression

IEEE Access, 2021
This paper proposes Maximal Associated Regression (MAR), a novel algorithm that performs forward stage-wise regression by applying nonlinear transformations to fit predictor covariates.
Sanush K. Abeysekera, Ye-Chow Kuang, Melanie Po-Leen Ooi, Vineetha Kalavally   +3 more
doaj   +1 more source

Regularization for Generalized Additive Mixed Models by Likelihood-Based Boosting [PDF]

, 2011
With the emergence of semi- and nonparametric regression the generalized linear mixed model has been expanded to account for additive predictors. In the present paper an approach to variable selection is proposed that works for generalized additive mixed
Groll, Andreas, Tutz, Gerhard
core   +2 more sources

Model-based Boosting in R: A Hands-on Tutorial Using the R Package mboost [PDF]

, 2012
We provide a detailed hands-on tutorial for the R add-on package mboost. The package implements boosting for optimizing general risk functions utilizing component-wise (penalized) least squares estimates as base-learners for fitting various kinds of ...
Hofner, Benjamin, Mayr, Andreas, Robinzonov, Nikolay, Schmid, Matthias   +3 more
core   +1 more source

Markov-switching generalized additive models

, 2015
We consider Markov-switching regression models, i.e. models for time series regression analyses where the functional relationship between covariates and response is subject to regime switching controlled by an unobservable Markov chain.
Glennie, Richard, Kneib, Thomas, Langrock, Roland, Michelot, Théo   +3 more
core   +1 more source

Impact of communities, health, and emotional-related factors on smoking use: comparison of joint modeling of mean and dispersion and Bayes’ hierarchical models on add health survey

BMC Medical Research Methodology, 2017
Background The analysis of correlated binary data is commonly addressed through the use of conditional models with random effects included in the systematic component as opposed to generalized estimating equations (GEE) models that addressed the random ...
Jie Pu, Di Fang, Jeffrey R. Wilson
doaj   +1 more source

Fast stable direct fitting and smoothness selection for Generalized Additive Models [PDF]

, 2007
Existing computationally efficient methods for penalized likelihood GAM fitting employ iterative smoothness selection on working linear models (or working mixed models).
Akaike H., Anderson E., Bowman A. W., Brezger A., Dennis J. E., Dongarra J. J., Fahrmeir L., Gill P. E., Golub G. H., Green P. J., Gu C, Gu C., Hastie T., Hastie T., McCullagh P., Parker R. L., Pinheiro J. C., R Development Core Team, Ramsay T., Ruppert D., Stone M., Wahba G., Wahba G., Watkins D. S., Wood S. N., Xiang D.   +25 more
core   +3 more sources

Smooth backfitting in generalized additive models

, 2007
Generalized additive models have been popular among statisticians and data analysts in multivariate nonparametric regression with non-Gaussian responses including binary and count data.
Mammen, Enno, Park, Byeong U., Yu, Kyusang   +2 more
core   +2 more sources