Additive models with autoregressive symmetric errors based on penalized regression splines

2021 ◽  
Author(s):  
Rodrigo A. Oliveira ◽  
Gilberto A. Paula
Keyword(s):  
Penalized Regression ◽  
Additive Models ◽  
Regression Splines ◽  
Penalized Regression Splines

scholarly journals Estimating the impact of vaccination using age–time-dependent incidence rates of hepatitis B

2007 ◽  
Vol 136 (3) ◽  
pp. 341-351 ◽  
Author(s):  
N. HENS ◽  
M. AERTS ◽  
Z. SHKEDY ◽  
P. KUNG'U KIMANI ◽  
M. KOJOUHOROVA ◽  
...  
Keyword(s):  
Hepatitis B ◽  
Generalized Additive Models ◽  
Additive Model ◽  
Penalized Regression ◽  
Incidence Rates ◽  
Additive Models ◽  
Time Dependent ◽  
Model Framework ◽  
Penalized Regression Splines ◽  
The Impact

SUMMARYThe objective of this study was to model the age–time-dependent incidence of hepatitis B while estimating the impact of vaccination. While stochastic models/time-series have been used before to model hepatitis B cases in the absence of knowledge on the number of susceptibles, this paper proposed using a method that fits into the generalized additive model framework. Generalized additive models with penalized regression splines are used to exploit the underlying continuity of both age and time in a flexible non-parametric way. Based on a unique case notification dataset, we have shown that the implemented immunization programme in Bulgaria resulted in a significant decrease in incidence for infants in their first year of life with 82% (79–84%). Moreover, we have shown that conditional on an assumed baseline susceptibility percentage, a smooth force-of-infection profile can be obtained from which two local maxima were observed at ages 9 and 24 years.

scholarly journals Climate sensitivity and resistance under pure- and mixed-stand scenarios in Lower Austria evaluated with distributed lag models and penalized regression splines for tree-ring time series

2019 ◽  
Vol 139 (2) ◽  
pp. 189-211 ◽  
Author(s):  
Arne Nothdurft ◽  
Markus Engel
Keyword(s):  
Tree Ring ◽  
Climate Sensitivity ◽  
European Beech ◽  
Penalized Regression ◽  
Mixed Stand ◽  
Regression Splines ◽  
Mixed Stands ◽  
Distributed Lag ◽  
Distributed Lag Models ◽  
Penalized Regression Splines

Abstract Penalized regression splines and distributed lag models were used to evaluate the effects of species mixing on productivity and climate-related resistance via tree-ring width measurements from sample cores. Data were collected in Lower Austria from sample plots arranged in a triplet design. Triplets were established for sessile oak [Quercus petraea (Matt.) Liebl.] and Scots pine (Pinus sylvestris L.), European beech (Fagus sylvatica L.) and Norway spruce [Picea abies (L.) H. Karst.], and European beech and European larch (Larix decidua Mill.). Mixing shortened the temporal range of time-lagged climate effects for beech, spruce, and larch, but only slightly changed the effects for oak and pine. Beech and spruce as well as beech and larch exhibited contrasting climate responses, which were consequently reversed by mixing. Single-tree productivity was reduced by between − 15% and − 28% in both the mixed oak–pine and beech–spruce stands but only slightly reduced in the mixed beech–larch stands. Measures of climate sensitivity and resistance were derived by model predictions of conditional expectations for simulated climate sequences. The relative climate sensitivity was, respectively, reduced by between − 16 and − 39 percentage points in both the beech–spruce and beech–larch mixed stands. The relative climate sensitivity of pine increased through mixing, but remained unaffected for oak. Mixing increased the resistance in both the beech–larch and the beech–spruce mixed stand. In the mixed oak–pine stand, resistance of pine was decreased and remained unchanged for oak.

scholarly journals S-Estimation for Penalized Regression Splines

2010 ◽  
Vol 19 (3) ◽  
pp. 609-625 ◽  
Author(s):  
Kukatharmini Tharmaratnam ◽  
Gerda Claeskens ◽  
Christophe Croux ◽  
Matias Salibián-Barrera
Keyword(s):  
Penalized Regression ◽  
Regression Splines ◽  
Penalized Regression Splines

Spatially Adaptive Bayesian Penalized Regression Splines (P-splines)

2005 ◽  
Vol 14 (2) ◽  
pp. 378-394 ◽  
Author(s):  
Veerabhadran Baladandayuthapani ◽  
Bani K Mallick ◽  
Raymond J Carroll
Keyword(s):  
Penalized Regression ◽  
Regression Splines ◽  
Spatially Adaptive ◽  
Penalized Regression Splines

Individual and Population Penalized Regression Splines for Accelerated Longitudinal Designs

Biometrics ◽  
2005 ◽  
Vol 61 (4) ◽  
pp. 1037-1048 ◽  
Author(s):  
Jaroslaw Harezlak ◽  
Louise M. Ryan ◽  
Jay N. Giedd ◽  
Nicholas Lange
Keyword(s):  
Penalized Regression ◽  
Regression Splines ◽  
Penalized Regression Splines

Corrigendum: Calibrating remotely sensed chlorophyll-a data by using penalized regression splines

2006 ◽  
Vol 55 (4) ◽  
pp. 551-552
Author(s):  
E. D. Clarke ◽  
D. C. Speirs ◽  
M. R. Heath ◽  
S. N. Wood ◽  
W. S. C. Gurney ◽  
...  
Keyword(s):  
Chlorophyll A ◽  
Penalized Regression ◽  
Remotely Sensed ◽  
Regression Splines ◽  
Penalized Regression Splines

scholarly journals S-Estimation for Penalized Regression Splines

2008 ◽  
Author(s):  
Kukatharmini Tharmaratnam ◽  
Christophe Croux ◽  
Gerda Claeskens ◽  
Matias Salibian-Barrera
Keyword(s):  
Penalized Regression ◽  
Regression Splines ◽  
Penalized Regression Splines
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