Prediction with Mixed Effects Smooth Models by using P-Splines
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Prediction with Mixed Effects Smooth Models by using P-Splines
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10102 - Applied mathematics
Result continuities
Project
—
Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
New Trends in Functional Statistics and Related Fields
ISBN
978-3-031-92382-1
ISSN
1431-1968
e-ISSN
2628-8966
Number of pages
9
Pages from-to
"511–519"
Publisher name
Springer
Place of publication
Cham
Event location
Novara, Itálie
Event date
Jun 25, 2025
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001545850800061