Boosting additive models using component-wise P-Splines
نویسندگان
چکیده
منابع مشابه
Boosting additive models using component-wise P-Splines
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 from a computational point of view. In particular, we give a detailed analysis on the effect of various P-spline hyper-parameters on the boosting...
متن کاملInference in generalized additive mixed models by using smoothing splines
Generalized additive mixed models are proposed for overdispersed and correlated data, which arise frequently in studies involving clustered, hierarchical and spatial designs. This class of models allows ̄exible functional dependence of an outcome variable on covariates by using nonparametric regression, while accounting for correlation between observations by using random effects. We estimate no...
متن کاملEM and component-wise boosting for Hidden Markov Models: a machine-learning approach to capture-recapture
1 This study presents a new boosting method for capture-recapture models, rooted in predictive2 performance and machine-learning. The regularization algorithm combines Expectation-Maximization and 3 boosting to yield a type of multimodel inference, including automatic variable selection and control of model 4 complexity. By analyzing simulations and a real dataset, this study shows the qualitat...
متن کاملFlexible estimation in cure survival models using Bayesian P-splines
In the analysis of survival data, it is usually assumed that any unit will experience the event of interest if it is observed for a sufficient long time. However, one can explicitly assume that an unknown proportion of the population under study will never experience the monitored event. The promotion time model, which has a biological motivation, is one of the survival models taking this featu...
متن کاملGeneralized structured additive regression based on Bayesian P-splines
Generalized additive models (GAM) for modelling nonlinear effects of continuous covariates are now well established tools for the applied statistician. In this paper we develop Bayesian GAM’s and extensions to generalized structured additive regression based on one or two dimensional P-splines as the main building block. The approach extends previous work by Lang and Brezger (2003) for Gaussian...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Computational Statistics & Data Analysis
سال: 2008
ISSN: 0167-9473
DOI: 10.1016/j.csda.2008.09.009