Instructor:
Todd Kuffner (
kuffner@math.wustl.edu)
Lecture: 2:304:00pm, Monday/Wednesday, Cupples I, Room 199
Office
Hours: By appointment.
Course
Overview:
This course is intended for Ph.D. students in
Statistics and Mathematics. Math 50615062 together form a yearlong
sequence
in mathematical statistics leading to the Ph.D. qualifying exam in
statistical theory. The first
semester will cover introductory measuretheoretic probability,
decision theory, notions of optimality, principles of data
reduction, and finite sample estimation and inference. We will discuss
foundational issues, and consider several paradigms for testing, such
as the NeymanPearson, neoFisherian, and Bayesian approaches. Roughly
half of the first semester is devoted to the measuretheoretic
foundations of probability theory and statistics. The
second
semester will cover asymptotic theory, including convergence in
measure, limit theorems, integral and density approximations, and
higherorder asymptotics. Maximum likelihood, Bayesian, and bootstrap
methods will be considered. Empirical processes, large deviations,
and modern topics (e.g. Bayesian nonparametric asymptotics) will be
introduced as time permits. The style of the
course
is theoremproof based; applications will not be emphasized, and
examples will be theoretical. Statistical software is not part of the
course.
Prerequisite:
It is assumed that students have taken a first course in real analysis,
probability, and mathematical statistics, and are familiar with basic
topology, multivariate calculus, and matrix algebra. Ph.D. students are
strongly encouraged to enroll in Math 50515052 concurrently (Ph.D.level
measure theory and functional analysis).
If you are undecided about whether or not to take this course, it may
be helpful to look at the
Ph.D.
qualifying exam
from the last time I taught the course (20142015). This time there
will be more measure theory and probability theory on exams.
Textbook:
There are many excellent books and online resources for the material in
this course. However, no single book is suitable. Due to the cost of
purchasing several books, I will not require that students use any
particular books. The recommended readings for each lecture are
accompanied by sections of three books listed below, but students are
welcome to look at other references for the same material. I will use
the same books for Math
5061 and Math 5062. The links give electronic access to two of the
books for Washington University students (logged in to library account)
through SpringerLink, but I also recommend purchasing these books as
they are excellent references for researchers.
 K.B. Athreya and S.N. Lahiri's
Measure
Theory and Probability Theory, First Edition, Springer. electronic
access errata
; thanks to Soumendra Lahiri for sending this to me!
 E.L. Lehmann and G. Casella's Theory
of Point Estimation, Second Edition, Springer. errata
more
errata
 E.L. Lehmann & J.P. Romano's Testing
Statistical Hypotheses, Third Edition, Springer. electronic
access errata
Another suggestion:
Essentials of
Statistical
Inference by
G.A. Young and R.L. Smith, Cambridge University Press. This book is
much shorter and not intended as an encyclopedic reference, but it is
perhaps the
most clearlywritten, insightful treatment of modern statistical
inference.
Homework:
There will be weekly homework assignments. You are strongly encouraged
to write your solutions
in LaTeX. If not, then handwritten submissions must be clear and
organized. Homework will be graded, but solutions will not be provided
to students.
Homework
grader: Qiyiwen Zhang (
qiyiwenzhang@wustl.edu)
Blackboard:
During the semester, homework assignments, homework and midterm exam
grades and any other courserelated announcements will be posted to
Blackboard or sent by email using Blackboard.
Attendance:
Attendance is required for all lectures. The student who misses a
lecture is responsible for any assignments and/or announcements
made.
Grades: 20% Homework, 25% Midterm 1, 25% Midterm 2, 30% Final
Exams: 2 inclass midterms and 1 final.
Homework: There will be regular homework assignments.
The lowest homework grade will be
dropped.
Final Course Grade: The letter grades for the course will be determined
according to the following numerical grades on a 0100 scale.
A+

impress me

B+

[87, 90)

C+

[77, 80)

D+

[67, 70)

F

[0,60)

A

93+

B

[83, 87)

C

[73, 77)

D

[63, 67)



A

[90, 93)

B

[80, 83)

C

[70, 73)

D

[60, 63)



Other
Course Policies: Students are encouraged to look at the Faculty
of Arts & Sciences
policies.
 Academic integrity:
Students
are expected to adhere to the University's policy
on academic
integrity.
 Auditing: There is an
option to audit, but this
still involves enrolling in the course. See the Faculty of Arts &
Sciences policy
on auditing.
Auditing students will still be expected to attend all
lectures and compete all required coursework and exams. A course grade
of 75 is required for a successful audit.
 Collaboration: Students
are encouraged to discuss homework with one another, but each student
must submit separate solutions, and these must be the original work of
the student.
 Exam conflicts: Read the
University policy.
The exam dates for this course are posted before the semester begins,
and thus you are expected to be present at all exams.
 Late homework: Only by
prior arrangement. If a valid reason for an exception is not presented
at least 36
hours before a homework due date, then it will not be accepted late (a
zero will be given for that assignment).
 Missed exams: There are
no makeup exams. For valid excused absences with midterm exams  such
as medical, family, transportation and weatherrelated
emergencies  the contribution of that midterm to the final course
grade will be redistributed equally to the other midterm exam and final
exam. Students missing both midterm exams and/or the final exam cannot
earn a passing grade for the course.
Course
Schedule:
will be updated after lecture to reflect what was actually covered;
AL=Athreya & Lahiri, TPE=Theory of Point Estimation (Lehmann &
Casella); TSP=Testing Statistical Hypotheses (Lehmann & Romano)
Lecture 1
NeymanPearson testing; optimal simple tests via likelihood ratios;
optimal onesided tests via monotone likelihood ratios
Reading: TSH 3.13.2, 3.4

Lecture 2
Optimal tests with composite nulls via least favorable priors
Reading: TSH 3.83.9

Lecture 3
Optimal unbiased testing (without nuisance parameters)
Reading: TSH 4.14.4, 3.6

Lecture 4
Optimal unbiased testing (with nuisance parameters); optimal invariant testing
Reading: TSH 4.14.4, 3.6

Lecture 5
Optimal invariant testing
Reading: TSH 6.16.3

Lecture 6
Computing Maximal Invariants in Stages; Confidence Regions
Reading: TSH 6.2, 5.4

Lecture 7
Some philosophy of science; interpretations of probability; paradigms of statistical inference; the pvalue controversy
Reading: TSH 3.3, plus
 Ronald L. Wasserstein & Nicole A. Lazar (2016). The ASA's Statements on pvalues: context, process, and purpose. American Statistician 70(2), 129133. Also, Supplement with 23 essays/comments. http://dx.doi.org/10.1080/00031305.2016.1154108

Lecture 8 Introduction to Likelihood
Sampling rules; principle of repeated sampling; parameterizations; likelihood quantities
Reading: TSH Chapter 12, TPE Chapter 6

Lecture 9 Likelihood Part II
MLE; identities under reparameterization; nonelementary likelihood examples
Reading: TSH Chapter 12, TPE Chapter 6
Other suggested references:
 G. Alastair Young and R. Smith (2005). Essentials of Statistical Inference, Cambridge University Press.
 O. BarndorffNielsen and D.R. Cox (1994). Inference and Asymptotics, Chapman & Hall.
 Tom Severini (2000). Likelihood Methods in Statistics, Oxford University Press.
 L. Pace and A. Salvan (1997). Principles of Statistical Inference, World Scientific.
 A. Azzalini (1996). Statistical Inference, Chapman & Hall.

Lecture 10 Fisherian Principles Part I Distribution constant statistics; sufficiency and likelihood principles.

Lecture 11 Fisherian Principles Part II
Completeness; conditionality principle; ancillary statistics; parameterization invariance.
Suggested reading:
 Anirban DasGupta, editor (2011). Selected Works of Debabrata Basu, Springer.
 Malay Ghosh (2002). Basu's theorem with applications: a personalistic review. Sankhya A 64(3), 509531.
 Chapter 3 of Michael Evans (2015). Measuring Statistical Evidence Using Relative Belief, Chapman & Hall.

Lecture 12 Some Distribution Theory
MGF and CGF; Levy and Fourier inversion theorems; cumulant generating function; stable distributions and selfdecomposable laws
Suggested references:
 Stuart & Ord (1948). Kendall's Advanced Theory of Statistics Volume 1, 6th edition, Wiley.
 Cramer (1946). Mathematical Methods of Statistics.
 Peter McCullagh (1987). Tensor Methods in Statistics, Chapman & Hall.
 John Kolassa (2006). Series Approximation Methods in Statistics, 3rd edition, Springer.
 Tom Severini (2005). Elements of Distribution Theory, Cambridge University Press.

Lecture 13 Multivariate Cumulants; Basic Stochastic Convergence
Joint cumulants; relationship between moments and cumulants; stochastic convergence definitions and examples
References: same as previous lecture

Lecture 14 Orders of Magnitude; Stochastic Orders of Magnitude
Definitions, examples; Slutsky's theorem; more stochastic convergence definitions and results
Suggested references:
 Lehmann's Elements of LargeSample Theory, Chapter 1
 Athreya & Lahiri; 8.18.4
 TSH Chapter 11

Lecture 15 Laws of Large Numbers
Asymptotic unbiasedness; consistency; several weak and strong LLNs;
revisiting the following theorems: continuous mapping, HellyBray,
dominated convergence
References: AL 8.18.4, 9.19.4, 10.110.4; TSH 11.2

Lecture 16 Asymptotic Normality
CramerWold, multivariate normal, Lindeberg and Lyapunov conditions
References: AL 10.110.4, 11.111.4; TSH 11.2

Lecture 17 CLT
LindebergFeller CLT for triangular arrays
References: Feller's book, Chapter VIII, Section 4 of Volume II
(Theorem 3 on p. 262) and also Chapter XV, Section 6 of Volume II
(Theorem 1 on p. 518); also AL 11.111.4

Lecture 18 Refinements to the CLT
BerryEsseen theorem; Cramer's condition; secondorder Edgeworth expansions
References: AL 11.4; Kolassa's book (Chapters 2 and 3); Bhattacharya & Rao's book (Chapters 3 and 4)

Lecture 19 Local Linear Approximations
Delta method; variancestabilizing transformations; sampling distributions of order statistics
References: Lehmann's book Elements of Large Sample Theory

Lecture 20 MLE Asymptotics and Efficiency
Consistency and asymptotic normality of MLE; asymptotic efficiency
References: Severini's book Likelihood Methods in Statistics

Lecture 21 Asymptotic theory for likelihoodbased inference

Lecture 22 Nonparametric bootstrap

April 28: Last day of spring semester classes
May 8: Final Exam
