The ability to formulate abstract concepts and draw conclusions from data is fundamental to mastering statistics. Aspects of Statistical Inference equips advanced undergraduate and graduate students with a comprehensive grounding in statistical inference, including nonstandard topics such as robustness, randomization, and finite population inference.
A. H. Welsh goes beyond the standard texts and expertly synthesizes broad, critical theory with concrete data and relevant topics. The text follows a historical framework, uses real–data sets and statistical graphics, and treats multiparameter problems, yet is ultimately about the concepts themselves.
Written with clarity and depth, Aspects of Statistical Inference:
- Provides a theoretical and historical grounding in statistical inference that considers Bayesian, fiducial, likelihood, and frequentist approaches
- Illustrates methods with real–data sets on diabetic retinopathy, the pharmacological effects of caffeine, stellar velocity, and industrial experiments
- Considers multiparameter problems
- Develops large sample approximations and shows how to use them
- Presents the philosophy and application of robustness theory
- Highlights the central role of randomization in statistics
- Uses simple proofs to illuminate foundational concepts
- Contains an appendix of useful facts concerning expansions, matrices, integrals, and distribution theory
Here is the ultimate data–based text for comparing and presenting the latest approaches to statistical inference.
Bayesian, Fiducial and Likelihood Inference.
Large Sample Theory.
Randomization and Resampling.
Principles of Inference.