Results 11 to 20 of about 43,843 (275)
Boosting Additive Models using Component-wise P-Splines [PDF]
Computational Statistics & Data Analysis, 2007 We consider an efficient approximation of Bühlmann & Yu’s L2Boosting algorithm with component-wise smoothing splines. Smoothing spline base-learners are replaced by P-spline base-learners which yield similar prediction errors but are more advantageous ...
Hothorn, Torsten, Schmid, Matthias
core +3 more sources
Journal of Computational and Graphical Statistics, 2001 P-splines are an attractive approach for modelling nonlinear smooth effects of covariates within the generalized additive and varying coefficient models framework.
Brezger, Andreas, Lang, S.
core +2 more sources
Nonparametric inference in hidden Markov models using P-splines [PDF]
Biometrics, 2013 Hidden Markov models (HMMs) are flexible time series models in which the distributions of the observations depend on unobserved serially correlated states.
Alexandrovich +25 more
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Simultaneous probability statements for Bayesian P-splines [PDF]
Statistical Modelling, 2005 P-splines are a popular approach for fitting nonlinear effects of continuous covariates in semiparametric regression models. Recently, a Bayesian version for P-splines has been developed on the basis of Markov chain Monte Carlo simulation techniques for
Brezger, Andreas, Lang, S.
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Semiparametric models and P-splines [PDF]
, 2001 P-splines were introduced by Eilers and Marx (1996). We consider semiparametric models where the smooth part of the model can be described by P-splines. A mixed model representation is also considered.
I., Currie,, M., Durbán,
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P-SPLINES USING DERIVATIVE INFORMATION. [PDF]
Multiscale Model Simul, 2010 Time series associated with single-molecule experiments and/or simulations contain a wealth of multiscale information about complex biomolecular systems. We demonstrate how a collection of Penalized-splines (P-splines) can be useful in quantitatively summarizing such data. In this work, functions estimated using P-splines are associated with stochastic
Calderon CP +3 more
europepmc +5 more sources
Bayesian nowcasting with Laplacian-P-splines
Journal of Computational and Graphical Statistics, 2022 AbstractDuring an epidemic, the daily number of reported infected cases, deaths or hospitalizations is often lower than the actual number due to reporting delays. Nowcasting aims to estimate the cases that have not yet been reported and combine it with the already reported cases to obtain an estimate of the daily cases. In this paper, we present a fast
Sumalinab B, Gressani O, Hens N, Faes C.
europepmc +4 more sources
A Data-Driven P-Spline Smoother and the P-Spline-Garch Models [PDF]
SSRN Electronic Journal, 2020 Penalized spline smoothing of time series and its asymptotic properties are studied. A data-driven algorithm for selecting the smoothing parameter is developed. The proposal is applied to define a semiparametric extension of the well-known Spline-GARCH, called a P-Spline-GARCH, based on the log-data transformation of the squared returns.
Feng, Yuanhua, Härdle, Wolfgang Karl
openaire +3 more sources
“Childhood Anemia in India: an application of a Bayesian geo-additive model”
BMC Pediatrics, 2021 Background The geographical differences that cause anaemia can be partially explained by the variability in environmental factors, particularly nutrition and infections.
Holendro Singh Chungkham +2 more
doaj +1 more source
Mathematics, 2023 Space-time varying coefficient models, which are used to identify the effects of covariates that change over time and spatial location, have been widely studied in recent years. One such model, called the quantile regression model, is particularly useful
Bertho Tantular +3 more
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