Classification of Continuous Distributional Data Using the Logratio Approach

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F22%3A73617310" target="_blank" >RIV/61989592:15310/22:73617310 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1201/9781315370545-9" target="_blank" >http://dx.doi.org/10.1201/9781315370545-9</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1201/9781315370545-9" target="_blank" >10.1201/9781315370545-9</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Classification of Continuous Distributional Data Using the Logratio Approach

Popis výsledku v původním jazyce

One of the fundamental tasks in statistics is to classify observations into one of predefined classes, whether the observations are of multivariate or functional character. When considering distributional data, suitable classification methods honouring the data specificities are yet to be developed. In this work, a classification method for distributional data represented as probability density functions (PDFs) is proposed. The method uses the centred log-ratio (clr) transformation to adapt functional linear discriminant analysis to the distributional setting. Within the proposed setting, each functional observation is projected into a reduced discriminant space, and the classification itself is then based on minimizing the distance between the linear projections of the class representatives and those of the functional observations. The introduced method is demonstrated on geological data consisting of particle size distribution of 250 soil samples from four measuring sites in Moravia region, Czech Republic.

Název v anglickém jazyce

Classification of Continuous Distributional Data Using the Logratio Approach

Popis výsledku anglicky

One of the fundamental tasks in statistics is to classify observations into one of predefined classes, whether the observations are of multivariate or functional character. When considering distributional data, suitable classification methods honouring the data specificities are yet to be developed. In this work, a classification method for distributional data represented as probability density functions (PDFs) is proposed. The method uses the centred log-ratio (clr) transformation to adapt functional linear discriminant analysis to the distributional setting. Within the proposed setting, each functional observation is projected into a reduced discriminant space, and the classification itself is then based on minimizing the distance between the linear projections of the class representatives and those of the functional observations. The introduced method is demonstrated on geological data consisting of particle size distribution of 250 soil samples from four measuring sites in Moravia region, Czech Republic.

Druh

C - Kapitola v odborné knize

CEP obor

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OECD FORD obor

10103 - Statistics and probability

Projekt

<a href="/cs/project/GA19-01768S" target="_blank" >GA19-01768S: Separace geochemických signálů v sedimentech: aplikace pokročilých statistických metod na rozsáhlé geochemické datové soubory</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název knihy nebo sborníku

Analysis of Distributional Data

ISBN

978-1-4987-2545-3

Počet stran výsledku

20

Strana od-do

183-202

Počet stran knihy

376

Název nakladatele

Chapman &amp; Hall

Místo vydání

London

Kód UT WoS kapitoly

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