Nonparametric algorithm for identification of outliers in environmental data

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/68081715:_____/18:00490001 RIV/60162694:G42__/18:00534993

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Nonparametric algorithm for identification of outliers in environmental data

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Nonparametric algorithm for identification of outliers in environmental data

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10700 - Other natural sciences

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název periodika

Journal of Chemometrics

ISSN

0886-9383

e-ISSN

—

Svazek periodika

32

Číslo periodika v rámci svazku

5

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

17

Strana od-do

"Nestrankovano"

Kód UT WoS článku

000434126600009

EID výsledku v databázi Scopus

2-s2.0-85041067993