Density Estimation Using the Sinc Kernel
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2026
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866909027066183680 |
|---|---|
| author | Glad, Ingrid Kristine Hjort, Nils Lid Ushakov, Nikolai G. |
| author_facet | Glad, Ingrid Kristine Hjort, Nils Lid Ushakov, Nikolai G. |
| contents | This paper deals with the kernel density estimator based on the so-called sinc (or Fourier integral) kernel $K(x)=(πx)^{-1}\sin x$. We study in detail both asymptotic and finite sample properties of this estimator. It is shown that, contrary to widespread opinion, the sinc estimator is superior to other estimators in many respects: it is more accurate for quite moderate values of the sample size, has better asymptotics in non-smooth case (the density to be estimated has only first derivative), is more convenient for the bandwidth selection, etc. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_07967 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Density Estimation Using the Sinc Kernel Glad, Ingrid Kristine Hjort, Nils Lid Ushakov, Nikolai G. Statistics Theory This paper deals with the kernel density estimator based on the so-called sinc (or Fourier integral) kernel $K(x)=(πx)^{-1}\sin x$. We study in detail both asymptotic and finite sample properties of this estimator. It is shown that, contrary to widespread opinion, the sinc estimator is superior to other estimators in many respects: it is more accurate for quite moderate values of the sample size, has better asymptotics in non-smooth case (the density to be estimated has only first derivative), is more convenient for the bandwidth selection, etc. |
| title | Density Estimation Using the Sinc Kernel |
| topic | Statistics Theory |
| url | https://arxiv.org/abs/2605.07967 |