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Prerequisite: Stat 410 or equivalent. You should be comfortable (after review)
with joint densities,
(multivariate, Jacobian) changes of variable, moment generating
functions, and conditional expectation.
Text: (Required) George Casella and Roger Berger
Statistical Inference, 2nd ed., Duxbury, 2002.
(Recommended)
V. Rohatgi and A.K. Saleh, An Introduction to Probability and Statistics,
2nd ed., Wiley, 2001.
Jun Shao, Mathematical Statistics, 2nd ed., Springer, 2003.
P. Bickel & K. Doksum Mathematical Statistics, vol.I, 2nd ed., Pearson Prentice Hall, 2007.
P. Billingsley,
Probability and Measure, 2nd (1986) or later edition, Wiley.
A sheet of errata in Casella & Berger compiled by Jeffrey Hart of
Texas A&M Stat Dept can be found here.
The most important of these errata are the ones on p.288, in Equation (6.2.7) and in line 14
(the last line
of Thm. 6.2.25): what is important as a sufficient condition for completeness is that the set of
"natural parameter"
values (η_{1},...,η_{k}) =
(w_{1}(θ),w_{2}(θ),...,w_{k}(θ)) fills out an open set
in R^{k} as
θ runs through all of Θ.
Course Coverage: This fall, we will cover the material in Chapters 1--4 fairly rapidly,
as review (in about four lectures). After that, we will cover most of Chapters 5-8. We spend some
time on Chap. 5 (going lightly over Sections 5.5.1-5.5.3, all other sections thoroughly),
covering Chapters 6-7 in full detail and going as far through Chapter 8 as we have time for.
This means that we will discuss properties of statistical procedures that are meaningful for small
or moderate samples reserving asymptotic techniques for large-sample data until the spring term,
and will revisit the Likelihood Ratio Test (going beyond the treatment in Casella and Berger)
for large-sample data. Further coverage of Bayes and empirical Bayes methods will be presented
in the spring along with material on confidence intervals. Computational topics will be treated
both terms, with EM algorithm and other topics related to likelihood maximization and basic
simulation in the fall, and bootstrap and Markov Chain Monte Carlo in the spring. Many of these
topics will be covered through handouts, during both terms.
Lecture Topics by Date.
Grading: There will be graded homework sets roughly every 1.5--2 weeks (7 altogether).
There will be two in-class tests,
the first on Wednesday October 8, covering material through
Chapter 5, and an in-class Final Exam. The course grade will
be based 30% on homeworks, 40% on
tests, and 30% on the Exam.
No Test make-ups will be offered. Homework will generally not be accepted late, and must
be handed in as hard-copy (except for
people off-campus when the due-dates are Fridays,
from whom electronic-format HW will be accepted as email attachments.
HONOR CODE
The University of Maryland, College Park has a nationally recognized
Code of Academic Integrity, administered by the Student Honor Council.
This Code sets standards for academic integrity at Maryland for all
undergraduate and graduate students. As a student you are responsible
for upholding these standards for this course. It is very important for
you to be aware of the consequences of cheating, fabrication,
facilitation, and plagiarism. For more information on the Code of
Academic Integrity or the Student Honor Council, please visit
http://www.shc.umd.edu.
To further exhibit your commitment to academic integrity, remember to
sign the Honor Pledge on all examinations and assignments:
"I pledge on
my honor that I have not given or received any unauthorized assistance
on this examination (assignment)."
Office Hours: My office hours will be Monday 11-12 and Wednesday 4-5:15.
I will often be available also at other times,
except on Tuesdays and Thursdays,
but please send an e-mail or arrange with me in
class for an office appointment.
Click here
to find a one-page summary of the course coverage and the first problem assignment.
For further information on the timing of individual lectures and tests,
click here and see the
Important
Dates below. For auxiliary reading in several useful handouts that are
described and
linked below, click here.
Click here
to find a cumulatively updated copy of all homework problem assignments.
Throughout the
term, partial problem set solutions will be posted
here.
(I) You can see
sample test problems for the 1st in-class test, along with the
Fall 2009 In-Class Test and a set of
sample problems for
the in-class final. Also see further sample Problems and Topics for the
Fall 2014 1st In-Class Test,
and sample Problems and Topics for the
Fall 2014 2nd In-Class Test.
(II) I have a paper on the topic of
distributions related to the normal that are or are not
uniquely determined by their moments.
The paper uses many of the techniques we review
in Chapters 2 to 4.
(III) The topic of mixture distributions and densities and their relation to
hierarchical specification
of a distributional model and to distribution functions of
mixed type is elaborated in this handout.
(IV) A handout on conjugate priors for a class of exponential family densities and probability mass functions.
(V) Handout on EM Algorithm from STAT 705.
(VI) Lecture Notes from Stat 705 on Numerical Maxmimization of Likelihoods.
(VII) Topics on Statistical Simulation: There are two sorts
of handouts on Simulation methods and
interpretation. First, under
this heading, there are 4 pdf writeups on Random Number Generation,
simulation, and interpretation of simulation experiments:
(i) Pseudo-random number
generation,
(ii) Transformation of
Random Variables, (iii) Statistical
Simulation , and if you want to read a little more
on
computational speedups in statistical
simulations, click here.
Topics (i) and (iv) were taken from my
web-pages for the course
STAT 705 on Statistical Computing in R.
Additional material on
statistical simulation for Bayesian MCMC is
discussed under heading (VI) below.
(VIII) Union-Intersection Tests covered in Casella and Berger are
discussed in a journal article in
connection with applications to so-called
Bioequivalence trials.
(IX) Background on Markov Chain Monte Carlo: First see
Introduction and application of MCMC
within an EM estimation problem
in random-intercept logistic regression. For additional pdf files of
"Mini-Course" Lectures, including computer-generated figures, see Lec.1 on Metropolis-Hastings
Algorithm,
and Lec.2 on the
Gibbs Sampler, with Figures that can be found in
Mini-Course Figure Folders.