Deep Quantile Aggregation

02/26/2021
by   Taesup Kim, et al.
0

Conditional quantile estimation is a key statistical learning challenge motivated by the need to quantify uncertainty in predictions or to model a diverse population without being overly reductive. As such, many models have been developed for this problem. Adopting a meta viewpoint, we propose a general framework (inspired by neural network optimization) for aggregating any number of conditional quantile models in order to boost predictive accuracy. We consider weighted ensembling strategies of increasing flexibility where the weights may vary over individual models, quantile levels, and feature values. An appeal of our approach is its portability: we ensure that estimated quantiles at adjacent levels do not cross by applying simple transformations through which gradients can be backpropagated, and this allows us to leverage the modern deep learning toolkit for building quantile ensembles. Our experiments confirm that ensembling can lead to big gains in accuracy, even when the constituent models are themselves powerful and flexible.

READ FULL TEXT

page 6

page 8

page 14

11/12/2021

Learning Quantile Functions without Quantile Crossing for Distribution-free Time Series Forecasting

Quantile regression is an effective technique to quantify uncertainty, f...
04/05/2022

Aggregating distribution forecasts from deep ensembles

The importance of accurately quantifying forecast uncertainty has motiva...
02/09/2021

Nonparametric C- and D-vine based quantile regression

Quantile regression is a field with steadily growing importance in stati...
03/04/2022

Uncertainty Estimation for Heatmap-based Landmark Localization

Automatic anatomical landmark localization has made great strides by lev...
01/23/2019

A Review on Quantile Regression for Stochastic Computer Experiments

We report on an empirical study of the main strategies for conditional q...
02/15/2019

Quantile double autoregression

Many financial time series have varying structures at different quantile...
01/30/2022

Joint Quantile Disease Mapping with Application to Malaria and G6PD Deficiency

Statistical analysis based on quantile regression methods is more compre...