arbiter
JournalsPricingAccount

On Kernel Density Derivative Estimation Near the Boundary

Hind Boureji & Abdullah Sayah

Journal of Statistical Research2026https://doi.org/10.3329/jsr.v59i2.88064article
ABDC C
Weight
0.50

What the paper says

Estimating density derivatives is a powerful technique in statistical data analysis. It has diverse applications in machine learning, signal processing, and statistical analysis. The kernel method is one of the most popular methods in nonparametric density derivative estimation, but this estimator is biased and is not consistent when the data are near the endpoints of the support. This paper investigates the challenge of estimating the first-order derivative of an unknown probability density function defined on the interval [0, 1]. We focus our study near the right boundary. The asymptotic properties are derived. A Monte Carlo study and real data example are provided to illustrate the finite sample performance of the proposed estimator. Journal of Statistical Research 2025, Vol. 59, No. 2, pp. 183-201

Open paper page →

Cite this paper

https://doi.org/https://doi.org/10.3329/jsr.v59i2.88064

Or copy a formatted citation

@article{hind2026,
  title        = {{On Kernel Density Derivative Estimation Near the Boundary}},
  author       = {Hind Boureji & Abdullah Sayah},
  journal      = {Journal of Statistical Research},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.3329/jsr.v59i2.88064},
}

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

Flag this paper

On Kernel Density Derivative Estimation Near the Boundary

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.