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.
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<strong>M. Rajagopalan</strong>, PhD, is Visiting Professor, Department of Statistics, Pondicherry University, Puducherry. Earlier, he served as Associate Professor of Statistics in the University of Minnesota, USA. With over four decades of teaching experience, Professor Rajagopalan has published several articles in national and international journals of repute. His areas of research include statistical inference and the Bayesian inference. <br><br> <strong>P. Dhanavanthan</strong>, PhD, is Professor and Head, Department of Statistics, Pondicherry University, Puducherry. Earlier, he served as Professor of Statistics in the University of Madras. Professor Dhanavanthan has over 25 years of teaching experience at undergraduate/postgraduate level and has published more than 20 research papers in national and international journals of repute. His areas of research include distribution theory, operation research, and queuing theory.
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