<abstract xmlns="http://www.w3.org/1999/xhtml"><p>The k-nearest neighbour kernel density estimationmethod is a special type of the kernel density estimation method with the local choice of the bandwidth. An advantage of this estimator is that smoothing varies according to the number of observations in a particular region. The crucial problem is how to estimate the value of the parameter k. In the paper we discuss the problem of choosing the parameter k in a way that minimizes the value of the asymptotic mean integrated square error (AMISE). We define the class of the modified cosine densities that meet the requirements given by the AMISE. The results are compared in a simulation study.</p></abstract>

The k-nearest neighbour kernel density estimationmethod is a special type of the kernel density estimation method with the local choice of the bandwidth. An advantage of this estimator is that smoothing varies according to the number of observations in a particular region. The crucial problem is how to estimate the value of the parameter k. In the paper we discuss the problem of choosing the parameter k in a way that minimizes the value of the asymptotic mean integrated square error (AMISE). We define the class of the modified cosine densities that meet the requirements given by the AMISE. The results are compared in a simulation study.

K-nearest neighbour kernel density estimation, the choice of optimal k

Masaryk University, Department of Mathematics and Statistics, Kotlářská 2, CZ–611-37, Brno, CZECH REPUBLIC

Tatra Mountains Mathematical Publications

{"article-title":"K-nearest neighbour kernel density estimation, the choice of optimal k"}

The k-nearest neighbour kernel density estimationmethod is a special type of the kernel density estimation method with the local choice of the bandwidth. An...

K-nearest neighbour kernel density estimation, the choice of optimal k

Online veröffentlicht: 13. Nov. 2012

Seitenbereich: 39 - 50

DOI: https://doi.org/10.2478/v10127-011-0035-z

Schlüsselwörter
kernel density estimation, k-nearest neighbour, modified cosine density.

This content is open access.

K-nearest neighbour kernel density estimation, the choice of optimal k

Online veröffentlicht: 13. Nov. 2012

Seitenbereich: 39 - 50

DOI: https://doi.org/10.2478/v10127-011-0035-z

Schlüsselwörterkernel density estimation, k-nearest neighbour, modified cosine density.

This content is open access.

Schlüsselwörter
kernel density estimation, k-nearest neighbour, modified cosine density.