All of Statistics
Larry Wasserman1. The book is suitable for graduate students in computer science and
honors undergraduates in math, statistics, and computer science. It is
also useful for students beginning graduate work in statistics who need
to fill in t heir background on mathematical statistics.
2. I cover advanced topics that are traditionally not taught in a first course.
For example, nonparametr ic regression, bootstrapping, density estima-
tion, and graphical models.
3. I have omitted topics in probability that do not play a central role in
statistical inference. For example, counting methods are virtually ab-
sent.
4. Whenever possible, I avoid tedious calculations in favor of emphasizing
concepts.
5. I cover nonparametric inference before parametric inference.
6. I abandon the usual "First Term = Probability" and "Second Term
= Statistics" approach . Some students only take the fi rst half and it
would be a crime if they did not see any statistical theory. Furthermore,
probability is more engaging when students can see it put to work in the
context of statistics. An exception is the topic of stochastic processes
which is included ill the later material.
7. T he course moves very quickly and covers much material . My colleagues
joke t hat I cover all of statistics in this course and hence the title. The
course is demanding but I have worked hard to make the material as
intuitive as possible so t hat the material is very understandable despite
the fast pace.
8. Rigor and clarity are not synonymous. I have tried to strike a good
balance. To avoid getting bogged down in uninteresting technical details,
many results are stated without proof. The bibliographic references at
the end of each chapter point the student to appropriate sources.