The book is known for its rigorous treatment of theoretical concepts, supported by numerous worked-out examples that help students understand the application of concepts like Likelihood Ratio Tests (LRT), Most Powerful Tests, and Neyman-Pearson lemma. Key Topics Covered in the Book
Covers both classical and Bayesian perspectives.
Dr. Manoj Kumar Srivastava, an Associate Professor of Statistics at the Institute of Social Sciences, Dr. B.R. Ambedkar University, has structured his curriculum across two definitive, highly detailed volumes.
┌───────────────────────────┐ │ Statistical Inference │ └─────────────┬─────────────┘ │ ┌──────────────────────┴──────────────────────┐ ▼ ▼ ┌──────────────────────────┐ ┌──────────────────────────┐ │ Theory of Estimation │ │ Testing of Hypotheses │ └──────────────────────────┘ └──────────────────────────┘ 1. Theory of Estimation
: Covers sufficiency, minimal sufficiency, and the Basu Theorem.
The sister textbook shifts focus toward decision-making under uncertainty, prioritizing the concepts established by J. Neyman and Egon Pearson. STATISTICAL INFERENCE: THEORY OF ESTIMATION