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This second edition focuses on modeling unbalanced data. It presents many new topics, including new chapters on logistic regression, log-linear models, and time-to-event data. It shows how to model main-effects and interactions and introduces nonparametric, lasso, and generalized additive regression models. The text carefully analyzes small unba
This textbook on Bayesian Statistics can be used to teach an upper division statistics course, a master's level course, a Ph.D. level course or used independently by researchers. It emphasizes the art of data modeling and analysis over technicalities and is rich with technical demonstrations available to Ph.D. students who need them and to others who are interested in them. The new edition features new material and new exercises and shifts the software focus from WinBUGS to OpenBUGS.
This second edition focuses on modeling unbalanced data. It presents many new topics, including new chapters on logistic regression, log-linear models, and time-to-event data. It shows how to model main-effects and interactions and introduces nonparametric, lasso, and generalized additive regression models. The text carefully analyzes small unbalanced data by using tools that are easily scaled to big data. R, Minitab®, and SAS codes are available on the author¿s website.
Emphasizing the use of WinBUGS and R to analyze real data, thsi book presents statistical tools to address scientific questions. It highlights foundational issues in statistics, the importance of making accurate predictions, and the need for scientists and statisticians to collaborate in analyzing data.
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