Yudi Pawitan graduated with a PhD in Statistics in 1987 from the University of California at Davis and has been Professor of Biostatistics since 2001 at the Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. He has worked in many areas of statistical applications, including time series analyses, medical imaging, and modelling and analysis of high-throughput molecular data. He has published more than 200 peer-reviewed research papers, split about equally between methodology and applied publications. He is co-author of the monograph Generalized Linear Models with Random Effects (2017) together with Youngjo Lee and John Nelder, which covers likelihood-based statistical modelling and inference in hierarchical GLMs. More recently, together with Youngjo Lee, he published Philosophies, Puzzles and Paradoxes, a book on statistical philosophy.

9780199671229

• 1: Introduction 2: Elements of likelihood in inference 3: More properties of likelihood 4: Basic models and simple applications 5: Frequentist properties 6: Modelling relationships: regression models 7: Evidence and the likelihood principle* 8: Score function and Fisher information 9: Large-sample results 10: Dealing with nuisance parameters 11: Complex data structures 12: EM Algorithm 13: Robustness of likelihood specification 14: Estimating equations and quasi-likelihood 15: Empirical likelihood 16: Likelihood of random parameters 17: Random and mixed effects models 18: Nonparametric smoothing Bibliography Index

Yudi Pawitan graduated with a PhD in Statistics in 1987 from the University of California at Davis and has been Professor of Biostatistics since 2001 at the Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. He has worked in many areas of statistical applications, including time series analyses, medical imaging, and modelling and analysis of high-throughput molecular data. He has published more than 200 peer-reviewed research papers, split about equally between methodology and applied publications. He is co-author of the monograph Generalized Linear Models with Random Effects (2017) together with Youngjo Lee and John Nelder, which covers likelihood-based statistical modelling and inference in hierarchical GLMs. More recently, together with Youngjo Lee, he published Philosophies, Puzzles and Paradoxes, a book on statistical philosophy.

9780199671229

• 1: Introduction 2: Elements of likelihood in inference 3: More properties of likelihood 4: Basic models and simple applications 5: Frequentist properties 6: Modelling relationships: regression models 7: Evidence and the likelihood principle* 8: Score function and Fisher information 9: Large-sample results 10: Dealing with nuisance parameters 11: Complex data structures 12: EM Algorithm 13: Robustness of likelihood specification 14: Estimating equations and quasi-likelihood 15: Empirical likelihood 16: Likelihood of random parameters 17: Random and mixed effects models 18: Nonparametric smoothing Bibliography Index