SEMIPARAMETRIC TRANSFORMATION FOR THEORETICAL MODEL

For fitting any theoretical model, we introduce the Power Transform-Both-sides (PTB) approach and the Power Transform-Both-sides and Weighted Least Squares (PTBWLS) approach which implements a power weighted transformation approach in PTB. Then, as an alternative to the PTB, we provide a Nonparametric Transform-Both-sides (NTB) approach to express function transformation as a cubic spline curve. As an estimation method which combines PTBWLS with together NTB, we propose a Nonparametric Transform-Both-sides and Weighted Least Squares (NTBWLS) approach. Through the numerical investigation of one example using data generated from a 1-compartment model, we conclude that PTB and PTBWLS induce normally distributed additive errors and stabilize the error variance, and NTBWLS improves the degrees of normality and homoscedasticity of the error more than PTB and PTBWLS.