Missing the Random Effect

When the Parameter Space Is Expanding

Autor*innen

• Ru Chen

Parameter

• 152 Seiten
• 6 Lesestunden

• Mathematik

Mehr zum Buch

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.

• Sprache: Englisch
• Erscheinungsdatum: 2012
• Einband: Paperback