On the asymptotic distribution of the least-squares estimators in unidentifiable models.
In order to analyze the stochastic property of multilayered perceptrons or other learning machines, we deal with simpler models and derive the asymptotic distribution of the least-squares estimators of their parameters. In the case where a model is unidentified, we show different results from traditional linear models: the well-known property of asymptotic normality never holds for the estimates of redundant parameters.
Hayasaka
Taichi
T
Department of Information and Computer Engineering, Toyota National College of Technology, Toyota, Aichi, Japan. hayasaka@toyota-ct.ac.jp
Kitahara
Masashi
M
Usui
Shiro
S
D000465:Algorithms; D001185:Artificial Intelligence; D016018:Least-Squares Analysis; D016014:Linear Models; D016571:Neural Networks, Computer; D015203:Reproducibility of Results; D013269:Stochastic Processes