Examples in Parametric Inference with R

By: Ulhas Jayram Dixit

Sign Up Now! Already a Member? Log In
You must be logged into Bookshare to access this title. Learn about membership options, or view our freely available titles.

Synopsis: This book discusses examples in parametric inference with R. Combining basic theory with modern approaches, it presents the latestdevelopments and trends in statistical inference for students who do not havean advanced mathematical and statistical background. The topics discussed inthe book are fundamental and common to many fields of statistical inference andthus serve as a point of departure for in-depth study. The book is divided intoeight chapters: Chapter 1 provides an overview of topics on sufficiency andcompleteness, while Chapter 2 briefly discusses unbiased estimation. Chapter 3focuses on the study of moments and maximum likelihood estimators, and Chapter 4presents bounds for the variance. In Chapter 5, topics on consistent estimatorare discussed. Chapter 6 discusses Bayes, while Chapter 7 studies some morepowerful tests. Lastly, Chapter 8 examines unbiased and other tests. Senior undergraduate and graduate students in statistics andmathematics, and those who have taken an introductory course in probability,will greatly benefit from this book. Students are expected to know matrixalgebra, calculus, probability and distribution theory before beginning thiscourse. Presenting a wealth of relevant solved and unsolved problems, the bookoffers an excellent tool for teachers and instructors who can assign homeworkproblems from the exercises, and students will find the solved examples hugelybeneficial in solving the exercise problems.