Boosting for statistical modelling-A non-technical introduction
From MaRDI portal
Publication:5142213
DOI10.1177/1471082X17748086OpenAlexW2790376794MaRDI QIDQ5142213
Publication date: 30 December 2020
Published in: Statistical Modelling (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1177/1471082x17748086
Related Items
Quantile regression: A short story on how and why, A primer on Bayesian distributional regression, A generalized additive model approach to time-to-event analysis, Semiparametric regression for discrete time-to-event data, Multiple smoothing parameters selection in additive regression quantiles, Transformation boosting machines
Uses Software
Cites Work
- Unnamed Item
- Model-based boosting in R: a hands-on tutorial using the R package mboost
- Geoadditive expectile regression
- Boosting algorithms: regularization, prediction and model fitting
- Boosting additive models using component-wise P-splines
- Statistical modeling: The two cultures. (With comments and a rejoinder).
- An update on statistical boosting in biomedicine
- Boosting flexible functional regression models with a high number of functional historical effects
- A unified framework of constrained regression
- Identifying Risk Factors for Severe Childhood Malnutrition by Boosting Additive Quantile Regression
- Variable Selection and Model Choice in Geoadditive Regression Models
- Quantile smoothing splines
- Boosting joint models for longitudinal and time‐to‐event data
- Quantile regression: A short story on how and why
- A primer on Bayesian distributional regression
- GAMLSS: A distributional regression approach
- Top-down transformation choice
- A generalized additive model approach to time-to-event analysis
- Semiparametric regression for discrete time-to-event data
- An introduction to semiparametric function-on-scalar regression
- Generalized Additive Modeling with Implicit Variable Selection by Likelihood‐Based Boosting
- Generalized Additive Models for Location, Scale and Shape