# Stability selection for component-wise gradient boosting in multiple dimensions

arXiv: https://arxiv.org/abs/1611.10171 · HTML: https://mlsanity.com/paper/1611.10171/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2016.

## Built on by (most cited first, 9 of 9)

- [Explaining Predictive Uncertainty with Information Theoretic Shapley Values](https://mlsanity.com/paper/2306.05724/) (2023)
- [A review of predictive uncertainty estimation with machine learning](https://mlsanity.com/paper/2209.08307/) (2022)
- [On the role of benchmarking data sets and simulations in method comparison studies](https://mlsanity.com/paper/2208.01457/) (2022)
- [Loss-guided Stability Selection](https://mlsanity.com/paper/2202.04956/) (2022)
- [Functional additive models on manifolds of planar shapes and forms](https://mlsanity.com/paper/2109.02624/) (2021)
- [Additive Covariance Matrix Models: Modelling Regional Electricity Net-Demand in Great Britain](https://mlsanity.com/paper/2211.07451/) (2022)
- [Scalable Estimation for Structured Additive Distributional Regression](https://mlsanity.com/paper/2301.05593/) (2023)
- [Stagewise Boosting Distributional Regression](https://mlsanity.com/paper/2405.18288/) (2024)
- [AdaPRL: Adaptive Pairwise Regression Learning with Uncertainty Estimation for Universal Regression Tasks](https://mlsanity.com/paper/2501.05809/) (2025)

## Abstract (arXiv)

We present a new algorithm for boosting generalized additive models for location, scale and shape (GAMLSS) that allows to incorporate stability selection, an increasingly popular way to obtain stable sets of covariates while controlling the per-family error rate (PFER). The model is fitted repeatedly to subsampled data and variables with high selection frequencies are extracted. To apply stability selection to boosted GAMLSS, we develop a new "noncyclical" fitting algorithm that incorporates an additional selection step of the best-fitting distribution parameter in each iteration. This new algorithms has the additional advantage that optimizing the tuning parameters of boosting is reduced from a multi-dimensional to a one-dimensional problem with vastly decreased complexity. The performance of the novel algorithm is evaluated in an extensive simulation study. We apply this new algorithm to a study to estimate abundance of common eider in Massachusetts, USA, featuring excess zeros, overdispersion, non-linearity and spatio-temporal structures. Eider abundance is estimated via boosted GAMLSS, allowing both mean and overdispersion to be regressed on covariates. Stability selection is used to obtain a sparse set of stable predictors.