Nonparametric algorithm for identification of outliers in environmental data
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43110%2F18%3A43913063" target="_blank" >RIV/62156489:43110/18:43913063 - isvavai.cz</a>
Alternative codes found
RIV/68081715:_____/18:00490001 RIV/60162694:G42__/18:00534993
Result on the web
<a href="http://dx.doi.org/10.1002/cem.2997" target="_blank" >http://dx.doi.org/10.1002/cem.2997</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1002/cem.2997" target="_blank" >10.1002/cem.2997</a>
Alternative languages
Result language
angličtina
Original language name
Nonparametric algorithm for identification of outliers in environmental data
Original language description
Outliers that can significantly affect data analysis are frequently present in environmental data sets. Most methods suggested for the detection of outliers impose restrictions on the distribution of analysed variables. However, in many environmental areas, the observed variable is influenced by a lot of different factors and its distribution is often difficult to find or cannot be estimated. Therefore, an approach for the identification of outliers in environmental time series based on nonparametric statistical techniques is presented. The core principle of the algorithm is to smoothen the data using nonparametric regression with variable bandwidth and subsequently analyse the residuals by nonparametric statistical methods. In the case that the distribution of the analysed variable is normal an efficient statistical method based on normality assumptions is presented as well. The proposed procedure is applied for the identification of outliers in hourly concentrations of particulate matter and verified by simulations.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10700 - Other natural sciences
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Journal of Chemometrics
ISSN
0886-9383
e-ISSN
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Volume of the periodical
32
Issue of the periodical within the volume
5
Country of publishing house
US - UNITED STATES
Number of pages
17
Pages from-to
"Nestrankovano"
UT code for WoS article
000434126600009
EID of the result in the Scopus database
2-s2.0-85041067993