Recovering Nonlinear Functions of Latent Variables: A Plausible-Value Neural Network Framework
恢复潜变量的非线性函数:合理值神经网络框架
When factor scores replace true latent scores in nonlinear prediction, measurement error attenuates the recoverable variance of any $k$th-order component of the regression function by $ρ^k$ -- the $k$th power of the score's coefficient of determination -- for any linear score type. This study derives the bound via Hermite polynomial expansion and proposes PV-ANN -- plausible values (posterior draws preserving latent variance) combined with artificial neural networks…