Practical, example-driven introduction to maximum likelihood for the social sciences. Emphasizes computation in R, model selection and interpretation.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Michael D. Ward is Professor Emeritus at Duke University, North Carolina. He has taught at Northwestern University, the University of Colorado, and the University of Washington. He worked as a principal research scientist at the Wissenschaftszentrum Berlin and held a Chaire Municipale at the University of Pierre Mendes France (Grenoble II). His work began with a study of the links between global and national inequalities, continued with seminal articles on the conflict processes in the Cold War, and more recently turned to analyses of networks of conflict and cooperation in the contemporary era. At Duke University, he established an innovative research lab of graduate and undergraduate students focusing on conflict prediction. One of the first political scientists to focus on the role of prediction in scholarly and policy work, he continues these efforts in his company, Predictive Heuristics, a data analytics firm that provides risk analysis for commercial and institutional clients.
Inhaltsangabe
Part I. Concepts, Theory, and Implementation: 1. Introduction to maximum likelihood 2. Theory 3. Maximum likelihood for binary outcomes 4. Implementing MLE Part II. Model Evaluation and Interpretation: 5. Model evaluation and selection 6. Inference and interpretation Part III. The Generalized Linear Model: 7. The generalized linear model 8. Ordered categorical variable models 9. Models for nominal data 10. Strategies for analyzing count data Part IV. Advanced Topics: 10. Duration 11. Strategies for missing data Part V. A Look Ahead: 13. Epilogue Index.
Part I. Concepts, Theory, and Implementation: 1. Introduction to maximum likelihood 2. Theory 3. Maximum likelihood for binary outcomes 4. Implementing MLE Part II. Model Evaluation and Interpretation: 5. Model evaluation and selection 6. Inference and interpretation Part III. The Generalized Linear Model: 7. The generalized linear model 8. Ordered categorical variable models 9. Models for nominal data 10. Strategies for analyzing count data Part IV. Advanced Topics: 10. Duration 11. Strategies for missing data Part V. A Look Ahead: 13. Epilogue Index.
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