| KRÁLÍK, Miroslav, Ondřej KLÍMA, Martin ČUTA, Robert M. MALINA, Sławomir KOZIEŁ, Lenka POLCEROVÁ, Anna ŠKULTÉTYOVÁ, Michal ŠPANĚL, Lubomír KUKLA and Pavel ZEMČÍK. Estimating Growth in Height from Limited Longitudinal Growth Data Using Full-Curves Training Dataset: A Comparison of Two Procedures of Curve Optimization—Functional Principal Component Analysis and SITAR. Children. Basel: MDPI, 2021, vol. 8, No 10, p. 1-21. ISSN 2227-9067. Available from: https://dx.doi.org/10.3390/children8100934. |
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@article{1802537,
author = {Králík, Miroslav and Klíma, Ondřej and Čuta, Martin and Malina, Robert M. and Kozieł, Sławomir and Polcerová, Lenka and Škultétyová, Anna and Španěl, Michal and Kukla, Lubomír and Zemčík, Pavel},
article_location = {Basel},
article_number = {10},
doi = {http://dx.doi.org/10.3390/children8100934},
keywords = {human growth; growth modelling; functional data analysis; Sitar},
language = {eng},
issn = {2227-9067},
journal = {Children},
title = {Estimating Growth in Height from Limited Longitudinal Growth Data Using Full-Curves Training Dataset: A Comparison of Two Procedures of Curve Optimization—Functional Principal Component Analysis and SITAR},
url = {https://www.mdpi.com/2227-9067/8/10/934},
volume = {8},
year = {2021}
}
TY - JOUR
ID - 1802537
AU - Králík, Miroslav - Klíma, Ondřej - Čuta, Martin - Malina, Robert M. - Kozieł, Sławomir - Polcerová, Lenka - Škultétyová, Anna - Španěl, Michal - Kukla, Lubomír - Zemčík, Pavel
PY - 2021
TI - Estimating Growth in Height from Limited Longitudinal Growth Data Using Full-Curves Training Dataset: A Comparison of Two Procedures of Curve Optimization—Functional Principal Component Analysis and SITAR
JF - Children
VL - 8
IS - 10
SP - 1-21
EP - 1-21
PB - MDPI
SN - 22279067
KW - human growth
KW - growth modelling
KW - functional data analysis
KW - Sitar
UR - https://www.mdpi.com/2227-9067/8/10/934
N2 - A variety of models are available for the estimation of parameters of the human growth curve. Several have been widely and successfully used with longitudinal data that are reasonably complete. On the other hand, the modeling of data for a limited number of observation points is problematic and requires the interpolation of the interval between points and often an extrapolation of the growth trajectory beyond the range of empirical limits (prediction). This study tested a new approach for fitting a relatively limited number of longitudinal data using the normal variation of human empirical growth curves. First, functional principal components analysis was done for curve phase and amplitude using complete and dense data sets for a reference sample (Brno Growth Study). Subsequently, artificial curves were generated with a combination of 12 of the principal components and applied for fitting to the newly analyzed data with the Levenberg–Marquardt optimization algorithm. The approach was tested on seven 5-points/year longitudinal data samples of adolescents extracted from the reference sample. The samples differed in their distance from the mean age at peak velocity for the sample and were tested by a permutation leave-one-out approach. The results indicated the potential of this method for growth modeling as a user-friendly application for practical applications in pediatrics, auxology and youth sport.
ER -
KRÁLÍK, Miroslav, Ondřej KLÍMA, Martin ČUTA, Robert M. MALINA, Sławomir KOZIEŁ, Lenka POLCEROVÁ, Anna ŠKULTÉTYOVÁ, Michal ŠPANĚL, Lubomír KUKLA and Pavel ZEMČÍK. Estimating Growth in Height from Limited Longitudinal Growth Data Using Full-Curves Training Dataset: A Comparison of Two Procedures of Curve Optimization—Functional Principal Component Analysis and SITAR. \textit{Children}. Basel: MDPI, 2021, vol.~8, No~10, p.~1-21. ISSN~2227-9067. Available from: https://dx.doi.org/10.3390/children8100934.
|
