The book provides a solid, well-thought and well-balanced foundation in probability and statistical inference, enabling students to gain fundamental understanding of the subject. The material is presented in a way that invokes the thinking process in students, and helps them find solutions to problems posed to them.
Mathematics is required for understanding probability, and probability is needed to understand statistical inference. Fisher presented a way to introduce methodologies and lay foundations in statistics, while Neyman showed how mathematics should be used in statistics to prove things mathematically and brought mathematical orientation in statistics. Topics covered in the book are fairly conventional along with some recent topics in probability and statistical inference presented in a rigorous mathematical and conceptual way. The aim of the book is to use principles of probability theory using tools of measure theory to build strong theoretical statistical inference. The topics have a wealth of material, and many topics are given greater in-depth coverage. The conceptual structure of topics is self-contained. Methodology is illustrated using real-life applications.
- Provides proofs for almost all the theorems and statements
- Material is backed by well-researched references
- Features more than 500 problems and more than 300 worked examples
- Includes R code for most of the worked examples
- Numerous figures and tables are used to help in understanding the concepts