An adaptive method for bandwidth selection in circular kernel
density estimation

J 2023

An adaptive method for bandwidth selection in circular kernel density estimation

ZÁMEČNÍK, Stanislav, Ivanka HOROVÁ, Stanislav KATINA and Kamila HASILOVÁ

Basic information

Original name

An adaptive method for bandwidth selection in circular kernel density estimation

Authors

ZÁMEČNÍK, Stanislav (703 Slovakia, belonging to the institution), Ivanka HOROVÁ (203 Czech Republic, belonging to the institution), Stanislav KATINA (703 Slovakia, belonging to the institution) and Kamila HASILOVÁ (203 Czech Republic)

Edition

Computational Statistics, Springer, 2023, 0943-4062

Other information

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

10103 Statistics and probability

Country of publisher

Germany

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

URL

Impact factor

Impact factor: 1.300 in 2022

RIV identification code

RIV/00216224:14310/23:00134765

Organization unit

Faculty of Science

DOI

http://dx.doi.org/10.1007/s00180-023-01401-0

UT WoS

001072240200001

Keywords in English

Circular density; Bandwidth selector; Adaptive kernel estimator; Von Mises density; Smoothed cross validation

Abstract

V originále

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.

Links

MUNI/A/1418/2019, interní kód MU

Name: Matematické a statistické modelování 4 (Acronym: MaStaMo4)

Investor: Masaryk University, Category A

Citovat

ZÁMEČNÍK, Stanislav, Ivanka HOROVÁ, Stanislav KATINA and Kamila HASILOVÁ. An adaptive method for bandwidth selection in circular kernel density estimation. Computational Statistics. Springer, 2023, vol. 39, No 4, p. 1709-1728. ISSN 0943-4062. Available from: https://dx.doi.org/10.1007/s00180-023-01401-0.

@article{2325437,
   author = {Zámečník, Stanislav and Horová, Ivanka and Katina, Stanislav and Hasilová, Kamila},
   article_number = {4},
   doi = {http://dx.doi.org/10.1007/s00180-023-01401-0},
   keywords = {Circular density; Bandwidth selector; Adaptive kernel estimator; Von Mises density; Smoothed cross validation},
   language = {eng},
   issn = {0943-4062},
   journal = {Computational Statistics},
   title = {An adaptive method for bandwidth selection in circular kernel density estimation},
   url = {https://link.springer.com/article/10.1007/s00180-023-01401-0},
   volume = {39},
   year = {2023}
}

TY  - JOUR
ID  - 2325437
AU  - Zámečník, Stanislav - Horová, Ivanka - Katina, Stanislav - Hasilová, Kamila
PY  - 2023
TI  - An adaptive method for bandwidth selection in circular kernel density estimation
JF  - Computational Statistics
VL  - 39
IS  - 4
SP  - 1709-1728
EP  - 1709-1728
PB  - Springer
SN  - 09434062
KW  - Circular density
KW  - Bandwidth selector
KW  - Adaptive kernel estimator
KW  - Von Mises density
KW  - Smoothed cross validation
UR  - https://link.springer.com/article/10.1007/s00180-023-01401-0
N2  - 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.
ER  -

ZÁMEČNÍK, Stanislav, Ivanka HOROVÁ, Stanislav KATINA and Kamila HASILOVÁ. An adaptive method for bandwidth selection in circular kernel density estimation. \textit{Computational Statistics}. Springer, 2023, vol.~39, No~4, p.~1709-1728. ISSN~0943-4062. Available from: https://dx.doi.org/10.1007/s00180-023-01401-0.

Detailed Information on Publication Record