Predicting the Whole Distribution with Methods for Depth Data Analysis Demonstrated on a Colorectal Cancer Treatment Study
DOI10.1007/978-981-15-1960-4_12zbMath1445.62294OpenAlexW2996860648MaRDI QIDQ3305499
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Publication date: 7 August 2020
Published in: Communications in Computer and Information Science (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/978-981-15-1960-4_12
Inference from stochastic processes and prediction (62M20) Nonparametric regression and quantile regression (62G08) Applications of statistics to biology and medical sciences; meta analysis (62P10) Exact distribution theory in statistics (62E15) Medical epidemiology (92C60)
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Cites Work
- Unnamed Item
- Model-based boosting in R: a hands-on tutorial using the R package mboost
- Boosting algorithms: regularization, prediction and model fitting
- Generalized extreme value regression for binary response data: an application to B2B electronic payments system adoption
- Additive models for quantile regression: model selection and confidence bands
- More honest foundations for data analysis
- Comparing Density Forecasts Using Threshold- and Quantile-Weighted Scoring Rules
- Computer Age Statistical Inference
- Quantile and Probability Curves Without Crossing
- On the Pooling of Time Series and Cross Section Data
- Beyond mean regression
- Discussion of ‘Beyond mean regression’
- Quantiles for Counts
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