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This book discusses examples in parametric inference with R.§Combining basic theory with modern approaches, it presents the latest§developments and trends in statistical inference for students who do not have§an advanced mathematical and statistical background. The topics discussed in§the book are fundamental and common to many fields of statistical inference and§thus serve as a point of departure for in-depth study. The book is divided into§eight chapters: Chap. 1 provides an overview of topics on sufficiency and§completeness, while Chap. 2 briefly discusses unbiased estimation. Chapter 3§focuses on the study of moments and maximum likelihood estimators, and Chap. 4§presents bounds for the variance. In Chap. 5, topics on consistent estimator§are discussed. Chapter 6 discusses Bayes, while Chap. 7 studies some more§powerful tests. Lastly, Chap. 8 examines unbiased and other tests.§§Senior undergraduate and graduate students in statistics and§mathematics, and those who have taken an introductory course in probability,§will greatly benefit from this book. Students are expected to know matrix§algebra, calculus, probability and distribution theory before beginning this§course. Presenting a wealth of relevant solved and unsolved problems, the book§offers an excellent tool for teachers and instructors who can assign homework§problems from the exercises, and students will find the solved examples hugely§beneficial in solving the exercise problems.§
