Preprocessing of centred logratio transformed density functions using smoothing splines

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F16%3A33158930" target="_blank" >RIV/61989592:15310/16:33158930 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1080/02664763.2015.1103706" target="_blank" >http://dx.doi.org/10.1080/02664763.2015.1103706</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1080/02664763.2015.1103706" target="_blank" >10.1080/02664763.2015.1103706</a>

Result language

angličtina

Original language name

Preprocessing of centred logratio transformed density functions using smoothing splines

Original language description

With large-scale database systems, statistical analysis of data, occurring in the form of probability distributions, becomes an important task in explorative data analysis. Nevertheless, due to specific properties of density functions, their proper statistical treatment of these data still represents a challenging task in functional data analysis. Namely, the usual L2 metric does not fully accounts for the relative character of information, carried by density functions; instead, their geometrical features are captured by Bayes spaces of measures. The easiest possibility of expressing density functions in an L2 space is to use centred logratio transformation, even though this results in functional data with a constant integral constraint that needs to be taken into account in further analysis. While theoretical background for reasonable analysis of density functions is already provided comprehensively by Bayes spaces themselves, preprocessing issues still need to be developed. The aim of this paper is to introduce optimal smoothing splines for centred logratio transformed density functions that take all their specific features into account and provide a concise methodology for reasonable preprocessing of raw (discretized) distributional observations. Theoretical developments are illustrated with a real-world data set from official statistics and with a simulation study

Czech name

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Czech description

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Type

J_x - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

CEP classification

BA - General mathematics

OECD FORD branch

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Project

<a href="/en/project/GA15-06991S" target="_blank" >GA15-06991S: Functional data analysis and related topics</a><br>

Continuities

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

Publication year

2016

Confidentiality

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

Name of the periodical

Journal of Applied Statistics

ISSN

0266-4763

e-ISSN

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Volume of the periodical

43

Issue of the periodical within the volume

8

Country of publishing house

GB - UNITED KINGDOM

Number of pages

17

Pages from-to

"1419-1435"

UT code for WoS article

000373938600004

EID of the result in the Scopus database

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