Extending models via gradient boosting: an application to Mendelian models

DOI10.1214/21-AOAS1482zbMath1478.62329arXiv2105.06559OpenAlexW3203065292MaRDI QIDQ2247455

Gregory Idos, Danielle Braun, Christine Hong, Theodore Huang, Giovanni Parmigiani, Stephen B. Gruber

Publication date: 17 November 2021

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2105.06559

zbMATH Keywords

risk modelgradient boostingMendelian model

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Applications of statistics to biology and medical sciences; meta analysis (62P10)

Uses Software

• BayesMendel
• ElemStatLearn
• DevCan
• XGBoost
• BOADICEA

Cites Work

• Greedy function approximation: A gradient boosting machine.
• Bagging predictors
• Additive logistic regression: a statistical view of boosting. (With discussion and a rejoinder by the authors)
• An Empirical Distribution Function for Sampling with Incomplete Information
• Maximum Likelihood Estimates of Monotone Parameters
• BayesMendel: an R Environment for Mendelian Risk Prediction
• Stochastic gradient boosting.

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