K. Zare

Evaluation of a robust and asymmetric class of nonlinear regression models under censoring

Censored nonlinear regression models are often based on Gaussian errors for analytical convenience. However, real data—such as income levels, HIV viral loads, or agricultural yields—often exhibit asymmetry, heavy tails, outliers, and censored observations, making classical Gaussian models inadequate. In this study, w...