Deep distribution regression

DOI10.1016/j.csda.2021.107203OpenAlexW3130324139MaRDI QIDQ830104

Brian J. Reich, Howard D. Bondell, Rui Li

Publication date: 7 May 2021

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1903.06023

zbMATH Keywords

machine learningconditional distributiondeep learningprobabilistic forecasting

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (2)

Marginally Calibrated Deep Distributional Regression ⋮ Bayesian nonparametric quantile process regression and estimation of marginal quantile effects

Uses Software

• Scikit
• TensorFlow
• PyTorch

Cites Work

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• Greedy function approximation: A gradient boosting machine.
• Approximating conditional density functions using dimension reduction
• On parameters of increasing dimensions
• Machine learning approaches for estimation of prediction interval for the model output.
• Sliced Inverse Regression for Dimension Reduction
• Nonparametric Estimation and Symmetry Tests for Conditional Density Functions
• Bayesian Density Estimation and Inference Using Mixtures
• Strictly Proper Scoring Rules, Prediction, and Estimation
• All of Nonparametric Statistics

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