Statistical Inference M. Rajagopal; P. Dhanavanthan

Preliminaries
Point Estimation—Unbiasedness and Consistency

Sufficiency and Completeness


Minimum Variance Unbiased Estimators



Methods of Estimation




Interval Estimation




Testing Statistical Hypotheses I





Testing Statistical Hypotheses II






Likelihood Ratio Method of Test Construction







Invariance and Equivariance







Bayesian Approach







Nonparametric Methods







Sequential Procedures

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.