Methods of Statistical Model Estimation

Programming and R Introduction R Specifics Programming Making R Packages Further Reading Statistics and Likelihood-Based Estimation Introduction Statistical Models Maximum Likelihood Estimation Interval Estimates Simulation for Fun and Profit Ordinary Regression Introduction Least-Squares Regression Maximum-Likelihood Regression Infrastructure Conclusion Generalized Linear Models Introduction GLM: Families and Terms The Exponential Family The IRLS Fitting Algorithm Bernoulli or Binary Logistic Regression Grouped Binomial Models Constructing a GLM Function GLM Negative Binomial Model Offsets Dispersion, Over and Under Goodness-of-Fit and Residual Analysis Weights Conclusion Maximum Likelihood Estimation Introduction MLE for GLM Two-Parameter MLE Panel Data What Is a Panel Model? Fixed-Effects Model Random-Intercept Model Handling More Advanced Models The EM Algorithm Further Reading Model Estimation Using Simulation Simulation: Why and When? Synthetic Statistical Models Bayesian Parameter Estimation Discussion Bibliography Index Exercises appear at the end of each chapter.