Missing the Random Effect

When the Parameter Space Is Expanding

Auteurs

• Ru Chen

Paramètres

• 152 pages
• 6 heures de lecture

• Mathématiques

En savoir plus sur le livre

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.

Achat du livre

• Année de publication: 2012
• Reliure: (souple)