Prediction with Mixed Effects Smooth Models by using P-Splines

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F25%3A73634022" target="_blank" >RIV/61989592:15310/25:73634022 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-031-92383-8_61" target="_blank" >http://dx.doi.org/10.1007/978-3-031-92383-8_61</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-92383-8_61" target="_blank" >10.1007/978-3-031-92383-8_61</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Prediction with Mixed Effects Smooth Models by using P-Splines

Popis výsledku v původním jazyce

This contribution explores the application of mixed effects smooth models to the analysis of real-world data with missing observations. These models combine P-splines with linear mixed models, allowing robust and interpretable predictions while providing control over the smoothness of the resulting curves through penalization. We first validate the functionality of the proposed approach on simulated data and subsequently apply the theoretical framework to a dataset describing the relationship between light reflectance and wavelength in trees. One of the main challenges in this dataset was the presence of missing values, which we addressed using a one-stage prediction approach that incorporates both observed and unobserved data. The results include smooth and accurate predictions, together with confidence intervals that reflect the variability of the predictions and provide additional information on the behaviour of the model and the uncertainty of the predictions.

Název v anglickém jazyce

Prediction with Mixed Effects Smooth Models by using P-Splines

Popis výsledku anglicky

This contribution explores the application of mixed effects smooth models to the analysis of real-world data with missing observations. These models combine P-splines with linear mixed models, allowing robust and interpretable predictions while providing control over the smoothness of the resulting curves through penalization. We first validate the functionality of the proposed approach on simulated data and subsequently apply the theoretical framework to a dataset describing the relationship between light reflectance and wavelength in trees. One of the main challenges in this dataset was the presence of missing values, which we addressed using a one-stage prediction approach that incorporates both observed and unobserved data. The results include smooth and accurate predictions, together with confidence intervals that reflect the variability of the predictions and provide additional information on the behaviour of the model and the uncertainty of the predictions.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10102 - Applied mathematics

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název statě ve sborníku

New Trends in Functional Statistics and Related Fields

ISBN

978-3-031-92382-1

ISSN

1431-1968

e-ISSN

2628-8966

Počet stran výsledku

9

Strana od-do

"511–519"

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Novara, Itálie

Datum konání akce

25. 6. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001545850800061