Bandwidth Selection Problem in Nonparametric Functional Regression
The focus of this paper is the nonparametric regression where the predictor is a functional random variable, and the response is a scalar. Functional kernel regression belongs to popular nonparametric methods used for this purpose. The two key problems in functional kernel regression are choosing...
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| Format: | Article |
| Language: | English |
| Published: |
Czech Statistical Office
2017-09-01
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| Series: | Statistika: Statistics and Economy Journal |
| Subjects: | |
| Online Access: | https://www.czso.cz/documents/10180/45606531/32019717q3107.pdf/d06c45a7-674c-4c4f-ac7b-dfea3293e915?version=1.0 |
| _version_ | 1811212550346375168 |
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| author | Daniela Kuruczová Jan Koláček |
| author_facet | Daniela Kuruczová Jan Koláček |
| author_sort | Daniela Kuruczová |
| collection | DOAJ |
| description | The focus of this paper is the nonparametric regression where the predictor is a functional random variable, and
the response is a scalar. Functional kernel regression belongs to popular nonparametric methods used for this
purpose. The two key problems in functional kernel regression are choosing an optimal smoothing parameter
and selecting an appropriate semimetric as a distance measure. The former is the focus of this paper – several
data-driven methods for optimal bandwidth selection are described and discussed. The performance of these
methods is illustrated in a real data application. A conclusion is drawn that local bandwidth selection methods
are more appropriate in the functional setting. |
| first_indexed | 2024-04-12T05:30:55Z |
| format | Article |
| id | doaj.art-0e92f666c40f48958186bc6bc00ed473 |
| institution | Directory Open Access Journal |
| issn | 0322-788X 1804-8765 |
| language | English |
| last_indexed | 2024-04-12T05:30:55Z |
| publishDate | 2017-09-01 |
| publisher | Czech Statistical Office |
| record_format | Article |
| series | Statistika: Statistics and Economy Journal |
| spelling | doaj.art-0e92f666c40f48958186bc6bc00ed4732022-12-22T03:46:04ZengCzech Statistical OfficeStatistika: Statistics and Economy Journal0322-788X1804-87652017-09-01973107115Bandwidth Selection Problem in Nonparametric Functional RegressionDaniela Kuruczová0Jan Koláček1Masaryk University, Brno, Czech RepublicMasaryk University, Brno, Czech RepublicThe focus of this paper is the nonparametric regression where the predictor is a functional random variable, and the response is a scalar. Functional kernel regression belongs to popular nonparametric methods used for this purpose. The two key problems in functional kernel regression are choosing an optimal smoothing parameter and selecting an appropriate semimetric as a distance measure. The former is the focus of this paper – several data-driven methods for optimal bandwidth selection are described and discussed. The performance of these methods is illustrated in a real data application. A conclusion is drawn that local bandwidth selection methods are more appropriate in the functional setting.https://www.czso.cz/documents/10180/45606531/32019717q3107.pdf/d06c45a7-674c-4c4f-ac7b-dfea3293e915?version=1.0Functional datanonparametric regressionkernel methodsbandwidth selection |
| spellingShingle | Daniela Kuruczová Jan Koláček Bandwidth Selection Problem in Nonparametric Functional Regression Statistika: Statistics and Economy Journal Functional data nonparametric regression kernel methods bandwidth selection |
| title | Bandwidth Selection Problem in Nonparametric Functional Regression |
| title_full | Bandwidth Selection Problem in Nonparametric Functional Regression |
| title_fullStr | Bandwidth Selection Problem in Nonparametric Functional Regression |
| title_full_unstemmed | Bandwidth Selection Problem in Nonparametric Functional Regression |
| title_short | Bandwidth Selection Problem in Nonparametric Functional Regression |
| title_sort | bandwidth selection problem in nonparametric functional regression |
| topic | Functional data nonparametric regression kernel methods bandwidth selection |
| url | https://www.czso.cz/documents/10180/45606531/32019717q3107.pdf/d06c45a7-674c-4c4f-ac7b-dfea3293e915?version=1.0 |
| work_keys_str_mv | AT danielakuruczova bandwidthselectionprobleminnonparametricfunctionalregression AT jankolacek bandwidthselectionprobleminnonparametricfunctionalregression |