Plug-in Optimization Method for Penalty Parameters in Nonparametric GMANOVA model
Abstract
In order to estimate the longitudinal trend, we often use the generalized multivariate analysis of variance (GMANOVA) model (Potthoff \& Roy, 1964), when the longitudinal data is balanced data. Usually, we use this GMANOVA model with some polynomial at the time of measurement. However, when the longitudinal trend has flexible curve, we cannot derive good fitting estimated curve when we use some polynomial curves. Then, Nagai (2011) proposed the nonparametric GMANOVA model which uses on several known basis functions instead of using the polynomial curves. If we use several basis functions, then overfitting problem is occurred. Nagai (2011) also proposed the estimation method for avoiding overfitting and unstable problems, and reducing computational iterative algorithm by extending the generalized ridge regression model (Yanagihara, Nagai \& Satoh, 2009). In the present paper, we extend one of the optimization methods in Nagai, Yanagihara and Satoh (2012) into the estimation method in Nagai (2011). Through numerical studies, we show some properties of each optimization method.
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BibTeX
@misc{nagai2026plugin,
title = {{Plug-in Optimization Method for Penalty Parameters in Nonparametric GMANOVA model}},
author = {Isamu Nagai},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.39206},
eprint = {2609.39206},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.39206},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Plug-in Optimization Method for Penalty Parameters in Nonparametric GMANOVA model
AU - Isamu Nagai
PY - 2026
DA - 2026/09/30
PB - arXiv
DO - 10.48550/arxiv.2609.39206
UR - https://arxiv.org/abs/2609.39206
AB - In order to estimate the longitudinal trend, we often use the generalized multivariate analysis of variance (GMANOVA) model (Potthoff \& Roy, 1964), when the longitudinal data is balanced data. Usually, we use this GMANOVA model with some polynomial at the time of measurement. However, when the longitudinal trend has flexible curve, we cannot derive good fitting estimated curve when we use some polynomial curves. Then, Nagai (2011) proposed the nonparametric GMANOVA model which uses on several known basis functions instead of using the polynomial curves. If we use several basis functions, then overfitting problem is occurred. Nagai (2011) also proposed the estimation method for avoiding overfitting and unstable problems, and reducing computational iterative algorithm by extending the generalized ridge regression model (Yanagihara, Nagai \& Satoh, 2009). In the present paper, we extend one of the optimization methods in Nagai, Yanagihara and Satoh (2012) into the estimation method in Nagai (2011). Through numerical studies, we show some properties of each optimization method.
ER -