The Annals of Statistics

Estimation of a Multivariate Mode

Thomas W. Sager

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Abstract

Consider a random sample from an absolutely continuous multivariate distribution. Let $\mathscr{J}$ be a class of sets which are not too long and thin. A point $\mathbf{\theta}_n$ chosen from a minimum volume set $S_n \in \mathscr{J}$ containing at least $r = r(n)$ of the data may be used as an estimate of the mode of the distribution. In this paper, it is shown that $\mathbf{\theta}_n$ converges almost surely to the true mode under very minor conditions on $\{r(n)\}$ and the distribution. Convergence rates are also obtained. Extensions to estimation of local and/or multiple modes are noted. Finally, computational simplifications resulting from choosing $S_n$ from spheres or cubes centered at observations are discussed.

Article information

Source
Ann. Statist., Volume 6, Number 4 (1978), 802-812.

Dates
First available in Project Euclid: 12 April 2007

Permanent link to this document
https://projecteuclid.org/euclid.aos/1176344253

Digital Object Identifier
doi:10.1214/aos/1176344253

Mathematical Reviews number (MathSciNet)
MR491553

Zentralblatt MATH identifier
0378.62037

JSTOR
links.jstor.org

Subjects
Primary: 62G05: Estimation
Secondary: 62H99: None of the above, but in this section 60F15: Strong theorems

Keywords
Estimation mode multivariate consistency convergence rates

Citation

Sager, Thomas W. Estimation of a Multivariate Mode. Ann. Statist. 6 (1978), no. 4, 802--812. doi:10.1214/aos/1176344253. https://projecteuclid.org/euclid.aos/1176344253


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