An adaptive method for bandwidth selection in circular kernel density estimation

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60162694%3AG42__%2F24%3A00560521" target="_blank" >RIV/60162694:G42__/24:00560521 - isvavai.cz</a>

Alternative codes found

RIV/00216224:14310/23:00134765

Result on the web

<a href="https://link.springer.com/article/10.1007/s00180-023-01401-0" target="_blank" >https://link.springer.com/article/10.1007/s00180-023-01401-0</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s00180-023-01401-0" target="_blank" >10.1007/s00180-023-01401-0</a>

Result language

angličtina

Original language name

An adaptive method for bandwidth selection in circular kernel density estimation

Original language description

Kernel density estimations of circular data are an effective type of nonparametric estimation. The performance of these estimations depends significantly on a smoothing parameter referred to as bandwidth. Selecting suitable bandwidths for these types of estimation pose fundamental challenges, therefore fixed bandwidth selectors are often the initial choice. The study investigates common bandwidth selection methods and proposes novel methods which adopt the idea from the linear case. The attention is also paid to variable bandwidth selection. Using simulations which incorporate a range of circular distributions that exhibit multimodality, peakedness and skewness, the proposed methods were evaluated and then compared with other bandwidth selectors to determine their potential advantages. Two real datasets, one containing animal movements and the other wind direction data, were applied to illustrate the utility of the proposed methods.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10103 - Statistics and probability

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Computational Statistics

ISSN

0943-4062

e-ISSN

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Volume of the periodical

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Issue of the periodical within the volume

September

Country of publishing house

DE - GERMANY

Number of pages

20

Pages from-to

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UT code for WoS article

001072240200001

EID of the result in the Scopus database

2-s2.0-85173054647