Summary:
Designed for a one-semester advanced undergraduate or graduate course, Statistical
Theory: A Concise Introduction clearly explains the underlying ideas and principles of major
statistical concepts, including parameter estimation, confidence intervals, hypothesis testing,
asymptotic analysis, Bayesian inference, linear models, nonparametric estimation, and elements of decision theory. It introduces these
topics on a clear intuitive level using illustrative examples in addition to the formal
definitions, theorems, and proofs.
Based on the authors’ lecture notes, the book is self-contained, which maintains a proper balance between the clarity and rigor of exposition.
In a few cases, the authors present a "sketched" version of a proof, explaining its main ideas rather than giving detailed technical mathematical
and probabilistic arguments.
Research Interests:
statistical learning
high-dimensional inference, sparsity, model selection procedures
nonparametric curve estimation and related problems