Rejoinder

DOI10.1002/bimj.201400010zbMath1441.62555OpenAlexW4238042191WikidataQ57338048 ScholiaQ57338048MaRDI QIDQ5891437

Andreas Ziegler

Publication date: 11 August 2014

Published in: Biometrical Journal (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/bimj.201400010

zbMATH Keywords

penalizationlogistic regressionmachine learningprobability estimation

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (1)

Probability estimation and machine learning-Editorial

Uses Software

• rms
• GeneSrF

Cites Work

• To explain or to predict?
• Classifier technology and the illusion of progress
• Probability estimation with machine learning methods for dichotomous and multicategory outcome: Theory
• Probability estimation with machine learning methods for dichotomous and multicategory outcome: Applications
• What subject matter questions motivate the use of machine learning approaches compared to statistical models for probability prediction?
• Machine learning versus statistical modeling
• Variable selection in large margin classifier-based probability estimation with high-dimensional predictors
• Class probability estimation for medical studies
• Risk prediction with machine learning and regression methods
• Maximum Likelihood Estimation of Misspecified Models
• Regression modeling strategies. With applications to linear models, logistic regression and survival analysis

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