General Estimating Function Theory

From the fully parametric setting to the semiparametric setting, General Estimating Function Theoryprovides a comprehensive introduction to the increasingly popular theory of estimating functions, which offers a unified framework for the study of many seemingly diverse methods of estimation. The book focuses on the significance of the modern theory rather than on mathematical rigor. It explores biostatistics topics, including artificial likelihoods, nuisance parameters, and model selection methods. The text also provides appendices on regularity conditions as well as Hilbert space and orthogonal projections.