The book delves into the implications of violating key assumptions in Maximum Likelihood estimation, focusing on cases where the true model is a mixed effect model while the working model is a fixed effect model with increasing parameter dimensions. It establishes conditions for the convergence of the Maximum Likelihood Estimator (MLE) to a normal distribution and introduces a robust variance estimator to address bias in sample variance. Additionally, it critiques automatic model selection methods and presents empirical studies to support theoretical findings in generalized linear models.
