scientific article; zbMATH DE number 7164729
From MaRDI portal
Publication:5214221
zbMath1440.62354arXiv1907.00825MaRDI QIDQ5214221
Ørnulf Borgan, Ida Scheel, Håvard Kvamme
Publication date: 7 February 2020
Full work available at URL: https://arxiv.org/abs/1907.00825
Title: zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Inference from stochastic processes and prediction (62M20) Linear regression; mixed models (62J05) Estimation in survival analysis and censored data (62N02) Point processes (e.g., Poisson, Cox, Hawkes processes) (60G55) Neural nets and related approaches to inference from stochastic processes (62M45)
Related Items (10)
Neural networks to predict survival from RNA-seq data in oncology ⋮ Survival Regression with Accelerated Failure Time Model in XGBoost ⋮ Special issue dedicated to Ørnulf Borgan ⋮ Bayesian Cox regression for large-scale inference with applications to electronic health records ⋮ Distributed Censored Quantile Regression ⋮ Fifty years with the Cox proportional hazards regression model ⋮ Unnamed Item ⋮ pycox ⋮ Continuous and discrete-time survival prediction with neural networks ⋮ Unnamed Item
Uses Software
Cites Work
- Unnamed Item
- Random survival forests
- Multi-step virtual metrology for semiconductor manufacturing: a multilevel and regularization methods-based approach
- Asymptotic theory for nested case-control sampling in the Cox regression model
- Comparison of the performance of neural network methods and Cox regression for censored survival data.
- Survival analysis. Techniques for censored and truncated data.
- Customer attrition analysis for financial services using proportional hazard models
- The maximum likelihood neural network as a statistical classification model
- Survival Model Predictive Accuracy and ROC Curves
This page was built for publication: