Statistical Inference: Classical and Bayesian Inference

Contents: 1. Sufficiency and completeness. 2. Unbiased estimations. 3. Efficiency of estimator. 4. Criteria and methods of estimation in large sample. 5. Decision theory. 6. Basic principles of testing of hypotheses. 7. Neyman theory. 8. Unbiased tests. 9. Similar tests. 10. Sequential probability ratio test (SPRT) and likelihood ration test (LRT). 11. Non - parametric methods. 12. UMP tests for truncated distributions. 13. Asymptotic tests for several exponential family of distribution. 14. Asymptotic tests for several truncated distributions. Appendix. References. Index.

This book is meant for BSc, BSc (Honors), and MSc students offer statistics as a primary subject. The book is very different from the existing books in many ways. Apart from regular topics covered in the Statistical Inference course, it also covers some of the author's research work. The book includes eleven chapters, out of which five chapters are on the theory of estimation, and the remaining are on testing of hypotheses. The purpose of this book is to give up-to-date exposition of Statistical Inference with less rigorous treatment on mathematics, giving realistic illustrations and simple proofs of the theorems so that the readers may easily digest it. The author's thirty-three years of teaching experience in postgraduate and refreshers courses programs enhanced the book's quality to a new level to have fresh expertise in statistics.