The Falling Factorial Basis and Its Statistical Applications
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Abstract (arXiv)
We study a novel spline-like basis, which we name the "falling factorial basis", bearing many similarities to the classic truncated power basis. The advantage of the falling factorial basis is that it enables rapid, linear-time computations in basis matrix multiplication and basis matrix inversion. The falling factorial functions are not actually splines, but are close enough to splines that they provably retain some of the favorable properties of the latter functions. We examine their application in two problems: trend filtering over arbitrary input points, and a higher-order variant of the two-sample Kolmogorov-Smirnov test.
Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). First posted on arXiv 3 May 2014.
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Citing papers per year: 2021: 2; 2022: 3; 2023: 1; 2024: 1; 2025: 0; 2026: 1.
8 papers.
- 2021Optimal Dynamic Regret in Exp-Concave Online Learningcited by 40
- 2022Optimal Dynamic Regret in Proper Online Learning with Strongly Convex Losses and Beyondcited by 28
- 2022Deep Learning meets Nonparametric Regression: Are Weight-Decayed DNNs Locally Adaptive?cited by 10
- 2021Multivariate Trend Filtering for Lattice Datacited by 4
- 2023Integral Probability Metrics Meet Neural Networks: The Radon-Kolmogorov-Smirnov Testcited by 2
- 2022A Cross Validation Framework for Signal Denoising with Applications to Trend Filtering, Dyadic CART and Beyondcited by 1
- 2024Distributed Learning with Discretely Observed Functional Datacited by 1
- 2026Data Fusion with Distributional Equivalence Test-then-poolcited by 0
- 2026Data Fusion with Distributional Equivalence Test-then-pool12 Mar 2026
- 2024Distributed Learning with Discretely Observed Functional Data3 Oct 2024
- 2023Integral Probability Metrics Meet Neural Networks: The Radon-Kolmogorov-Smirnov Test5 Sep 2023
- 2022Deep Learning meets Nonparametric Regression: Are Weight-Decayed DNNs Locally Adaptive?20 Apr 2022
- 2022Optimal Dynamic Regret in Proper Online Learning with Strongly Convex Losses and Beyond21 Jan 2022
- 2022A Cross Validation Framework for Signal Denoising with Applications to Trend Filtering, Dyadic CART and Beyond7 Jan 2022
- 2021Multivariate Trend Filtering for Lattice Data29 Dec 2021
- 2021Optimal Dynamic Regret in Exp-Concave Online Learning23 Apr 2021