arbiter
JournalsAccount

Random irregular histograms

Oskar Høgberg Simensen et al.

Computational Statistics and Data Analysis2026https://doi.org/10.1016/j.csda.2026.108367preprint
AJG 3ABDC A
Weight
0.50

Abstract

We propose a new method of histogram construction, providing a fully Bayesian approach to irregular histograms. Our procedure applies Bayesian model selection to a piecewise constant model of the underlying distribution, resulting in a method that selects both the number of bins as well as their location based on the data in a fully automatic fashion. We show that the histogram estimate is consistent with respect to the Hellinger metric under mild regularity conditions, and that it attains a convergence rate equal to the minimax rate (up to a logarithmic factor) for Hölder continuous densities. Simulation studies indicate that the new method performs comparably to other histogram procedures, both for minimizing the estimation error and for identifying modes. A software implementation is included as supplementary material.

Open via your library →

Cite this paper

https://doi.org/https://doi.org/10.1016/j.csda.2026.108367

Or copy a formatted citation

@article{oskar2026,
  title        = {{Random irregular histograms}},
  author       = {Oskar Høgberg Simensen et al.},
  journal      = {Computational Statistics and Data Analysis},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1016/j.csda.2026.108367},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

Random irregular histograms

Flags are reviewed by the Arbiter methodology team within 5 business days.

Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.