## Abstract

Kernel estimation provides an unbinned and non-parametric estimate of the probability density function from which a set of data is drawn. In the first section, after a brief discussion on parametric and non-parametric methods, the theory of kernel estimation is developed for univariate and multivariate settings. The second section discusses some of the applications of kernel estimation to high-energy physics. The third section provides an overview of the available univariate and multivariate packages. This paper concludes with a discussion of the inherent advantages of kernel estimation techniques and systematic errors associated with the estimation of parent distributions.

Original language | English (US) |
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Pages (from-to) | 198-207 |

Number of pages | 10 |

Journal | Computer Physics Communications |

Volume | 136 |

Issue number | 3 |

DOIs | |

State | Published - May 15 2001 |

## Keywords

- HEPUKeys
- KEYS
- Kernel estimation
- Multivariate probability density estimation
- Non-parametric
- PDE
- RootPDE
- Unbinned
- WinPDE

## ASJC Scopus subject areas

- Hardware and Architecture
- Physics and Astronomy(all)