Intended as a text for the postgraduate students of statistics, this well-written book gives a complete coverage of Estimation theory and Hypothesis testing, in an easy-to-understand style. It is the outcome of the authors’ teaching experience over the years. The text discusses absolutely continuous distributions and random sample which are the basic concepts on which Statistical Inference is built up, with examples that give a clear idea as to what a random sample is and how to draw one such sample from a distribution in real-life situations. It also discusses maximum-likelihood method of estimation, Neyman’s shortest confidence interval, classical and Bayesian approach. The difference between statistical inference and statistical decision theory is explained with plenty of illustrations that help students obtain the necessary results from the theory of probability and distributions, used in inference.

Preface
1. Preliminaries
2. Point Estimation—Unbiasedness and Consistency
3. Sufficiency and Completeness
4. Minimum Variance Unbiased Estimators
5. Methods of Estimation
6. Interval Estimation
7. Testing Statistical Hypotheses I
8. Testing Statistical Hypotheses II
9. Likelihood Ratio Method of Test Construction
10. Invariance and Equivariance
11. Bayesian Approach
12. Nonparametric Methods
13. Sequential Procedures
Appendix • Index