Use this url to cite publication: https://hdl.handle.net/20.500.14172/24579
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Discriminant analysis of environmental data based on zero inflated spatial auto-beta models
Type of publication
Straipsnis Web of Science ir Scopus duomenų bazėje / Article in Web of Science and Scopus database (S1)
Type of document
type::text::journal::journal article::research article
Author(s)
Title
Discriminant analysis of environmental data based on zero inflated spatial auto-beta models
Publisher
Oxford : Elsevier
Date Issued
Date Issued | Volume | Issue | Start Page | End Page |
---|---|---|---|---|
2023 | vol. 53 | art. no. 100724 | 1 | 10 |
Is part of
Spatial statistics
Field of Science
Abstract
In this paper, a supervised classification problem of classifying
feature observation into one of two populations is considered.
Classical supervised classification in this work is expanded for
non-Gaussian spatial data specified by zero inflated auto-beta
models. A classification rule based on the Bayes discriminant
function (BDF) that uses conditional probability density functions
in the expression is proposed. This rule is applied to real data
set for identifying sea bottom type by using Black carrageen
concentration data over the southeastern Baltic Sea. Different
feature observation models are chosen and classification results
are compared using a hold-out error rate measure.
feature observation into one of two populations is considered.
Classical supervised classification in this work is expanded for
non-Gaussian spatial data specified by zero inflated auto-beta
models. A classification rule based on the Bayes discriminant
function (BDF) that uses conditional probability density functions
in the expression is proposed. This rule is applied to real data
set for identifying sea bottom type by using Black carrageen
concentration data over the southeastern Baltic Sea. Different
feature observation models are chosen and classification results
are compared using a hold-out error rate measure.
ISSN (of the container)
2211-6753
WOS
000917390300001
Scopus
2-s2.0-85144961645
Other Identifier(s)
1-s2.0-S2211675322000859
Coverage Spatial
Jungtinė Karalystė / United Kingdom of Great Britain and Northern Ireland (GB)
Language
Anglų / English (en)
Bibliographic Details
25
Date Reporting
2023
Journal | IF | AIF | AIF (min) | AIF (max) | Cat | AV | Year | Quartile |
---|---|---|---|---|---|---|---|---|
Spatial Statistics | 2.3 | 3.775 | 1.9 | 5.5 | 4 | 0.739 | 2022 | Q1 |
Journal | IF | AIF | AIF (min) | AIF (max) | Cat | AV | Year | Quartile |
---|---|---|---|---|---|---|---|---|
Spatial Statistics | 2.3 | 3.775 | 1.9 | 5.5 | 4 | 0.739 | 2022 | Q1 |
3.775 | ||||||||
3.749 | ||||||||
3.734 |
Journal | Cite Score | SNIP | SJR | Year | Quartile |
---|---|---|---|---|---|
Spatial Statistics | 3.6 | 1.158 | 0.662 | 2022 | Q1 |