استنباط آماری: یک رویکرد یکپارچه

Presenting an integrated look at the two main schools of statistical thought: the frequentist and the Bayesian, this book's main purpose is to show the parallels existing between the results given by both methodologies. The two schools are compared, contrasted and examined in the light of the most recent developments in theoretical and applied statistics research. This book is also an update on the field of statistics in general, with discussions of new research using approximation and computationally-intensive methods. From the graduate level, to those working in the related areas of Economics, Mathematics and Engineering this volume is a much needed guide to an integrated view of Statistics. Presenting an integrated approach to Statistical Inference at a graduate level, this book discusses and compares the two main schools of statistical thought which are: frequentist and Bayesian. It covers such subjects as point and interval estimation, hypotheses testing and prediction, while also exploring computationally-intensive methods. Introduction -- Elements Of Inference -- Prior Distribution -- Estimation -- Approximate And Computationally Intensive Methods -- Hypothesis Testing -- Prediction -- Introduction To Linear Models. H.s. Migon And D. Gamerman. Includes Bibliographical References (p. [253]-255) And Indexes. This graduate-level text on statistical inference is unique in providing a detailed comparison of frequentist and Bayesian approaches. It covers recent computational methods, methods for point and interval estimation, and hypothesis testing and prediction.