Academic Programs
Department of Statistics
Texas A&M University
Download the Academic Program Information (PDF)
1) Graduate Program in Statistics
4) Doctor of Philosophy Program
6) Faculty
7) Undergraduate Course Offerings
Graduate Program in
Statistics
The Department of
Statistics offers a graduate program, leading to the degrees of Master of
Science and Doctor of Philosophy. The Department also jointly sponsors graduate
work with all subject matter area departments in setting up flexible minor
programs in statistics.
The Department of
Statistics offers two options in its master’s degree programs: (1) the Master
of Science degree (thesis option) which requires the preparation of a thesis
and (2) the Master of Science (nonthesis option) which requires more formal course
work in lieu of the thesis. Within either option, students are allowed to
choose either a broadbased or specialized program of study. All choices,
however, provide a balanced training in statistical methods, computational statistics,
and statistical theory, and are intended to prepare the student to adapt
statistical methodologies to practical problems.
The aim of the Ph.D.
program is to provide comprehensive and balanced training in statistical
methods, computational statistics, and the theory of statistics. Particular
emphasis is placed on training students to independently recognize the
relevance of statistical methods to the solution of specific problems and to
enable them to develop new methods when they are needed. The training aims
to convey a sound knowledge of existing statistical theory, including the
mathematical facility to develop new results in statistical methodology. At the
same time, the program is kept sufficiently flexible to permit students to
develop their specific interests.
NonThesis
Option
A student seeking the
Master of Science degree under the NonThesis Option must fulfill the following
requirements:
A.
Coursework. Note that STAT 601, 651, 652, and 658 may not be used to
fulfill any of the coursework requirements listed below.
1)
STAT
604, 608, 641 and 642.
2)
One
credit hour of STAT 681.
3)
Two
semester credit hours of statistical consulting experience (STAT 684) earned in
a minimum of two semesters. Rules governing completing this requirement are
given below in Item B.
4)
Three
semester credit hours of STAT 685 for the preparation of a special problem (see
item C below).
5)
Eighteen
semester credit hours based on one of the emphasis areas outlined below.
6)
A
total of 36 semester credit hours.
BroadBased Plan
1)
STAT
610, 611
2)
Four
additional approved statistics courses.
Biostatistics Emphasis
1)
STAT
610, 611, 645 and 646
2)
Two
additional approved statistics courses.
Computational Emphasis
1)
STAT
610 and 611
2)
Two
courses in Mathematics (e.g., MATH 609, MATH 610 or MATH 660).
3)
Two
courses in Computer Science (e.g., CPSC 603, CPSC 654 or CPSC 659).
Applied Emphasis
1)
STAT
630, 636, 657, 659
2)
Two
additional approved statistics courses.
B. Consulting
Experience. One semester credit hour of STAT 684 can be obtained through the
completion of any of the experiences listed below.
1)
One
semester of service in the Statistical Consulting Center.
2)
One
semester of a departmentally approved internship.
3)
Special
experiences, with prior approval from the Department Head or the Associate
Department Head that involves the following:
a. At
least one semester of activity
b. The application of
statistical knowledge
c. Working with
nonstatisticians
d. Sufficient statistical
supervision.
C. Form a
master’s advisory committee and complete a special project under the direction
of the chairman of the advisory committee. Three semester credit hours of STAT
685 are earned by completion of this project. Upon completion, the student is required
to compose a written report and make an oral presentation on the work. The
purpose of this project is to familiarize the student with the type of problems
that may be encountered in future work and to give the student a chance to
develop the ability to present results both verbally and in writing. In many
cases, work done during an internship may be used as the basis for the
student’s master’s project. However, this project must be completed under the supervision
of the chairperson of the student’s advisory committee.
D. Pass the departmental MS examination (see
below).
E. Pass a
final oral examination. This examination is concerned with the student’s
coursework and special problem. It is administered by the student’s advisory committee.
Thesis Option
A student seeking the
Master of Science degree under the Thesis Option must fulfill the following requirements:
A.
Coursework. Note that STAT 601, 651, 652, and 658 may not be used to
fulfill any of the coursework requirements listed below.
1)
STAT
604, 608, 610, 611, 641 and 642.
2)
One
credit hour of STAT 681.
3)
Six
semester credit hours of STAT 691 for preparation of a thesis (see item B
below).
4)
Nine
semester credit hours based on one of the emphasis areas outlined below. STAT 691
semester credit hours may not be used to satisfy this requirement.
5)
A
total of 34 semester credit hours.
BroadBased Plan
1)
Three
additional approved statistics course.
Biostatistics Emphasis
1)
STAT
643 and 644
2)
One
additional approved statistics course.
Computational Emphasis
1)
At
least one course in Mathematics (e.g., MATH 609, MATH 610 or MATH 660).
2)
At
least one course in Computer Science (e.g., CPSC 603, CPSC 654 or CPSC 659).
B. Form
an advisory committee and complete a thesis under the direction of the chairman
of the advisory committee. The
Department does not insist that this represent an original contribution to the
field of statistics. It is intended to train the student in carrying out
independently a piece of research; this may represent an application of
existing statistical methods in a new area or a comparative evaluation of statistical
methods.
C. Pass the departmental MS examination (see
below).
D. Pass a
final oral examination. This examination is concerned with the student’s coursework
and thesis. It is administered by the student’s advisory committee.
Master’s
Diagnostic Examination
The MS examination
covers basic statistical methods. The examination is evaluated with the
performance judged to be “Pass” or “Fail.” To receive a Master’s Degree, a
student must take and pass the exam.
The diagnostic exam is
offered twice a year–prior to the beginning of the fall semester and prior to
the beginning of the spring semester, and must be taken at the earliest
possible time after the student has completed the required courses: STAT 610,
611 (or 630), 604, 608, 641, and 642. Any exception to this time limit must be
obtained in writing from the head of the Department.
The results of a
student’s examination are reported to the faculty of the Statistics Department.
If the student’s performance is judged to be deficient, the examination may be retaken
the next time it is offered. Only one retake of the examination is allowed.
The breadth of the
field of statistics as well as the frontiers of knowledge in a particular
research area are emphasized in the Ph.D. program. The student seeking a Ph.D.
in statistics is required to fulfill the following requirements. A Ph.D.
selection committee will examine the background of entering students to
determine if they have the appropriate mathematics/statistics background to
successfully complete the program. Those students determined to have the
appropriate background will need to complete the courses under the Fast Track
and the remaining students will take the courses under the Traditional Track.
A.
Courses. Note that STAT 601, 623, 651, 652, 653, and 658 may not be used to fulfill any of the coursework requirements listed below.
1)
Required
Courses – Fast Track: STAT 605, 612,
613, 614, 620, 632, 648, (621 or 642).
2)
Required
Courses – Traditional Track: STAT 604, 608,
610, 611, 641, 642, 605, 612, 613, 614, 620, 632, 648.
3)
At
least four courses from the elective course list provided below with, at most,
two from the Applied Electives.
4)
If
entering with a Bachelor’s degree then complete three additional courses from either
elective course lists.
5)
For
those students selecting the Methodology / Applications emphasis (see Ph.D.
Examinations section), two semester credit hours of statistical consulting
experience (STAT 684) earned in a minimum of two semesters. Rules governing
acceptable methods for completing this requirement are given below in Item B.
No consulting experience is required of students selecting the
Theory/Methodology emphasis.
6)
Four
semester credit hours of STAT 681.
7)
A
sufficient number of research hours, STAT 691, to achieve a total of at
least 96 semester credit hours beyond a Bachelor’s degree or 64 semester credit
hours beyond a Master’s degree.
Applied Electives
607, 626, 631, 636,
647, 656, 657, 659, 661
Theory Electives
615, 616, 618, 621, 627,
633, 643, 644, 662, 665, 673, 674, 689
MATH 607, 615, 625, 628
B.
Consulting Experience. One semester credit hour of STAT 684 can be obtained
through the completion of any of the experiences listed below.
1)
One
semester of service in the Statistical Consulting Center.
2)
One
semester of a departmentally approved internship.
3)
Special
experiences, with prior approval from either the Department Head or the
Associate Department Head that involve the following:
a. At least one
semester of activity
b. The application of
statistical knowledge
c. Working with
nonstatisticians
d. Sufficient
statistical supervision.
C. Ph. D. Examinations:
Ph.D.
Students in Fast Track Program:
There will be three
examinations covering Theory (614, 620), Methodology (612, 613), Applied /
Computational Statistics (605, 648). The exams are evaluated as Pass or Fail. To
continue in the Ph.D. program, a student must pass two of the three exams.
The exams will be
offered during August of every year. Students must take at least two out of the
three exams during the first August following their completion of four courses associated
with two areas of concentration. Students who did not pass both exams during
their first attempt may take any of the three exams during their second taking
of the exams. If a student fails one exam and passes one exam, then during
their second taking of the exam they will only need to pass one exam. They may
take the exam they failed or they may take the exam that was not taken in their
first attempt. All students must take the exams during the first August
following their completion of four courses associated with two areas of
concentration.
Each year, after the
Ph.D. exams are graded, the Ph.D. Examination Committee will report the results
of the exams to the faculty. The committee will also make recommendations
concerning the appropriate
advisement of those students who did not pass the required number of exams. A
report of these recommendations will also be presented to the
faculty prior to meeting with the individual students. Based on the results of
the exams and the student’s performance in their first year Ph.D. courses, the
students will be given one of several options concerning their continuation in
the Ph.D. program.
Possible Options given
to students:
1)
Continue
in the Ph.D. program by taking the second year of Ph.D. courses and a retake of
the exams in the next August.
2)
Discontinue
the Ph.D. program and take the necessary M.S. courses to receive an M.S. in
statistics.
If after two attempts a
student has not passed two of the exams, the student will not be allowed to
continue for a Ph.D. in our program. However, the student will be given the
option to complete the requirements for an M.S. in statistics.
Ph.D. Students in Traditional Track Program:
Ph.D. students in the
Traditional Track will not take the Ph.D. exam at the end of their first year. Instead,
they will take the MS Diagnostic Exam at the end of their first year. The Ph.D.
Examination committee will review the performance of all students
with respect to their performance in courses and their performance on the M.S.
Diagnostic Examination. Based on this review, a recommendation will be made
concerning the continuation of these students in the Ph.D. program. Those
students who continue in the Ph.D. program will follow the Fast Track program as
described above which would include taking the Ph.D. exams during the first
August following their completion of four courses associated with two areas of
concentration.
D. Form a
Ph.D. advisory committee and pass the Preliminary Examination administered by
the advisory committee (see below).
E. Write
a Ph.D. Dissertation and pass the final Defense of Dissertation Examination
(see below). The student is also required to present the results of their
research in a regularly scheduled departmental seminar.
Preliminary Examination
After a student has
passed two of the three departmental Ph.D. written examinations, a student
needs to select a faculty member of the Department of Statistics to be her/his
Ph.D. advisor and to direct his/her research. The student and the selected
faculty member should work together to form a Ph.D. advisory committee
consisting of two more members of the statistics faculty and a faculty member
outside of the department of statistics. A Ph.D. degree plan is then submitted
to the Office of Graduate Studies (OGS). This degree plan must be approved by
the OGS before the student is allowed to take the preliminary exam.
The preliminary
examination consists of the following parts:
1)
A
written examination developed by the statistics members of the student’s
advisory committee. This exam is usually waived at the discretion of the
departmental committee members.
2)
A
written examination administered by the member of the student’s advisory
committee from outside the Statistics Department. This examination is usually
waived.
3)
An
oral examination administered by the members of the student’s advisory
committee. The oral exam generally consists of the student presenting a
proposal for his/her Ph.D. dissertation research and discussing any research
results completed at the point of the exam.
Scheduling the
preliminary examination can only take place after the student has an approved
Ph.D. degree plan on file with OGS. The student must successfully pass the
preliminary examination at least 14 weeks prior to the date of the dissertation
defense. The results of the examination are reported to OGS using the Report of Doctoral Preliminary Examination, a form found at the
OGS website.
The Ph.D. Dissertation
After successfully
completing the course work and the preliminary examination, a period of time is
to be devoted to a research topic in either statistical methodology or
statistical theory under the guidance of the student’s advisor.
The results of this research must be communicated in a written dissertation
satisfying the guidelines established by the University. The research must
constitute an original contribution to the science of statistics and may
derive new results in statistical theory or methodology or may be concerned
with developing statistical methodology in new areas of application.
Once the student’s
advisor feels that the student has completed the dissertation, a final oral
examination is conducted by the advisory committee in which the student defends
the dissertation.
Students after one year
of coursework are eligible to participate in an internship with a sponsoring
company, hospital, or federal agency. The internships are generally a semester’s
stay at the sponsor’s site. If a student participates in one of the internship
programs approved by the department head, then:
1)
The
student is given credit for one hour of STAT 684.
2)
In
many cases, work done during the internship may be used as the basis for a
Master’s project. However, this project must be completed under the supervision
of the chairperson of the student’s advisory committee.
·
S. J. Sheather, Professor and Head; Ph.D. in Statistics: LaTrobe University, 1986;
development of regression diagnostics and robust and flexible regression
methods, statistical models of wine quality.
·
M. T. Longnecker, Professor and Associate Head; Ph.D. in Statistics: Florida State University,
1976; statistical education and consulting.
·
D. Akleman, Senior Lecturer;
Ph.D. in Agricultural Economics: Texas A&M University, 1996; time series,
stochastic processes, risk analysis, artificial intelligence, econometrics.
·
A. Bhattacharya, Assistant Professor; Ph.D.
in Statistics: Duke Univeristy, 2012; Factor models, Gaussian process,
highdimensional data, large contingency tables.
·
J. H. Carroll, Senior Lecturer; MS
in Statistics: Texas A&M University, 1990; Statistics education.
·
R. J. Carroll, Distinguished
Professor; Ph.D. in Statistics: Purdue University, 1974; longitudinal data,
measurement error, nutritional epidemiology, bioinformatics.
·
W. Chen, Professor; Ph.D. in
Statistics: New York University, 2001; long memory time series, econometrics
·
D. B. H. Cline, Professor; Ph.D. in
Statistics: Colorado State University, 1983; Nonlinear time series, Markov chains, subexponential distributions, Tauberian theorems, heavytailed
distributions.
·
A. Dabney, Associate Professor;
Ph.D. in Biostatistics: University of Washington, 2006; microarrays, bioinformatics, classification methods.
·
P. F. Dahm, Professor and
Graduate Advisor; Ph.D. in Statistics: Iowa State University, 1979; measurement
error models, biostatistics, econometrics.
·
R. J. Freund, Professor Emeritus;
Ph.D. in Experimental Statistics: North Carolina State University, 1955; statistical
data analysis, applications of regression and linear models.
·
C. E. Gates, Professor Emeritus;
Ph.D. in Experimental Statistics: North Carolina State University, 1955; design
and analysis of experimental data, estimation of wildlife abundance and modeling
nonlinear growth curves.
· J. D. Hart, Professor; Ph.D. in Statistics: Southern
Methodist University, 1981; nonparametric function estimation, time series,
bootstrap methods.
·
K. Hatfield, Lecturer; MBA in
Operations Research: North Texas
State University, 1980; Statistics education and consulting.
·
R. R. Hocking, Professor Emeritus;
Ph.D. in Statistics: Iowa State University, 1962; regression, mixed models and
multivariate analysis.
·
J. Huang, Professor; Ph.D. in
Statistics: University of California, Berkeley, 1997; nonparametric and
semiparametric methods, statistical function estimation using polynomial
splines, statistical methods for longitudinal data/panel data,
multivariate/functional data analysis, survival analysis, duration data, event
history analysis, statistics application in business.
·
O. C. Jenkins, Professor Emeritus;
Ph.D. in Statistics: Texas A&M University, 1972; statistical sampling and
experimental design.
·
V. E. Johnson, Professor of
Statistics; Ph.D. in Statistics: University of Chicago, 1989; longitudinal
data, nonparametric statistics, applied statistics, biostatistics, categorical
data, Bayesian methods.
·
E. R. Jones, Executive Professor,
Ph.D. in Statistics: Virginia Polytechnic and State University, 1976; applied
statistics, statistical computing, data mining/machine learning.
·
M. Jun, Associate Professor,
Ph.D. in Statistics: University of Chicago, 2005; statistical methodologies,
environmental problems, spacetime covariance modeling, numerical model
evaluation in air quality problems, combining numerical model output with
observed data.
·
M. Katzfuss, Assistant Professor;
Ph.D. in Statistics: Ohio State University, 2011; Spatial and spatiotemporal
statistics, Bayesian inference, massive datasets, probabilistic forecasting,
applications to environmental and genetic data.
·
E. Y. Kolodziej, Senior Lecturer,
Ph.D. in Statistics: Texas A&M University, 2010; spatial statistics,
statistics education, consulting.
·
S. Lahiri, Professor; Ph.D. in
Statistics: Michigan State University, 1989; asymptotic expansions,
environmental statistics, resampling methods, spatial statistics, small area
estimation, time series, wavelets.
·
F. Liang, Professor, Ph.D. in
Statistics: University of Hong Kong, 1998; Bayesian computation and
bioinformatics.
·
J. P. Long, Assistant Professor,
Ph.D. in Statistics: University of California, Berkeley, 2013; Applied statistics,
machine learning, time series, classification, astrostatistics,
errors in variables.
·
Y. Ma, Professor, Ph.D. in
Statistics: Massachusetts Institute of Technology, 1999; semiparametric
methods, mixed effect models with nonnormally distributed random effect,
skewelliptical distributions, HIV modeling and analysis, inverse problem using
Markov Chain Monte Carlo approach.
·
B. Mallick, Distinguished Professor;
Ph.D. in Statistics: University of Connecticut, 1994; Bayesian hierarchical
modeling, nonparametric regression and classification, bioinformatics, spatiotemporal
modeling, machine learning, functional data analysis, Bayesian nonparametrics,
petroleum reservoir characterization, uncertainty analysis of computer model
outputs.
·
J. H. Matis, Professor Emeritus;
Ph.D. in Statistics: Texas A&M University, 1970; biomathematics,
compartmental analysis, statistical ecology and applied stochastic processes.
·
U. MüllerHarknett, Professor;
Ph.D. in Mathematics: University of Bremen, 1997; non and semiparametrics,
efficient estimation.
·
H. J. Newton, Professor and Dean;
Ph.D. in Statistics: State University of New York at Buffalo, 1975; time series
analysis, computational statistics.
·
E. Parzen, Distinguished
Professor Emeritus; Ph.D. in Mathematics: University of California (Berkeley),
1953; statistical sciencedeveloping statistical methods for time series
analysis, data analysis, and change analysis.
·
M. Pourahmadi, Professor, Ph.D. in
Statistics: Michigan State University, 1980, time series analysis and
prediction theory, multivariate analysis, longitudinal data analysis,
mixedeffects models, data mining, stochastic volatility models.
·
L. J. Ringer, Professor Emeritus;
Ph.D. in Statistics: Texas A&M University, 1966; applied statistics, survey
sampling and reliability.
·
H. Sang, Assistant Professor,
Ph.D. in Statistics: Duke University, 2008; Bayesian
statistics with focus on spatial and spatiotemporal statistics.
·
H. Schmiediche, Senior Lecturer; Ph.D.
in Statistics: Texas A&M University, 1993; computational statistics.
·
M. Sherman, Professor; Ph.D. in
Statistics: University of North Carolina at Chapel Hill, 1992; biostatistics,
spatial statistics.
·
S. Sinha, Associate Professor,
Ph.D. in Statistics: University of Florida, 2004; methodological research:
missing data technique, measurement error, splines, Bayesian methods:
parametric and nonparametric methods, application: epidemiology, genetic
epidemiology.
·
W. B. Smith, Professor Emeritus;
Ph.D. in Statistics: Texas A&M University, 1967; multivariate analysis,
missing data methods, correspondence analysis.
·
F. M. Speed, Professor; Ph.D. in
Statistics: Texas A&M University, 1969; computational statistics,
biostatistics, linear models, applied statistics, multivariate methods,
environmental and industrial statistics, teaching statistics real time.
·
C. H. Spiegelman, Distinguished Professor;
Ph.D. in Statistics & Applied Mathematics: Northwestern University, 1976;
calibration curves, measurement error models, applied statistics, especially to
chemistry.
·
S. Subba Rao, Associate Professor;
Ph.D. in Statistics: University of Bristol, UK, 2001; time series,
nonstationary processes, nonlinear processes, recursive online algorithms,
spatiotemporal models.
·
E. Toby, Senior Lecturer;
Ph.D. in Mathematics: University of California, San Diego, 1988; biostatistics,
diffusions processes.
·
S. Wang, Professor; Ph.D. in
Statistics: University of Texas at Austin, 1988; biostatistical inferences,
missing and mismeasured data modeling and analysis, non and semiparametric
methodology, resampling methods, small sample asymptotics, survey sampling.
·
T. E. Wehrly, Professor; Ph.D. in
Statistics: University of Wisconsin, 1976; stochastic models, directional data,
mathematical statistics, nonparametric function estimation.
·
W. West, Professor; Ph.D. in Statistics:
Rice University, 1994; computational and graphical statistics, toxicological
risk assessment, Nonparametric statistics, stochastic modeling.
· L. Zhou, Assistant Professor, Ph.D. in Statistics:
University of California, 1997; statistical Methodology and
application in bioinformatics, nutrition and epidemiology,
functional/longitudinal data analysis.
· J. Zinn, Professor of Mathematics and Statistics; Ph.D. in Mathematics:
University of Wisconsin, 1972; empirical processes, bootstrapping.
Undergraduate Course
Offerings
201.
Elementary Statistical Inference. (30). Credit 3. Data collection,
tabulation, and presentation. Elementary description of the tools of
statistical inference; probability, sampling, and hypothesis testing.
Applications of statistical techniques to practical problems. May not be taken
for credit after or concurrently any other course in statistics or INFO 303 has
been taken.
211.
Principles of Statistics I. (30). Credit 3. Introduction
to probability and probability distributions. Sampling and descriptive
measures. Inference and hypothesis testing. Linear regression, analysis of
variance. Prerequisite: MATH 152 or 172.
212.
Principles of Statistics II. (30). Credit 3. Design
of experiments, model building, multiple regression, nonparametric techniques, contingency
tables, and short introductions to response surfaces, decision theory and time
series data. Prerequisite: STAT 211.
301.
Introduction to Biometry. (30). Credit 3. Intended
for students in animal sciences. Introduces fundamental concepts of biometry
including measures of location and variation, probability, tests of
significance, regression, correlation, and analysis of variance which are used
in advanced courses and are being widely applied to animaloriented industry.
Credit will not be allowed for more than one of STAT 301, 302 or 303. Prerequisite:
MATH 141 or 166 or equivalent.
302.
Statistical Methods. (30). Credit 3. Intended
for undergraduate students in the biological sciences and agriculture (except agricultural
economics). Introduction to concepts of random sampling and statistical inference;
estimation and testing hypotheses of means and variances; analysis of variance;
regression analysis; contingency tables. Credit will not be allowed for more
than one of STAT 301, 302 or 303. Prerequisite: MATH 141 or 166 or equivalent.
303.
Statistical Methods. (30). Credit 3. Intended for undergraduate students in the
social sciences. Introduction to concepts of random sampling and statistical
inference, estimation and testing hypotheses of means and variances, analysis
of variance, regression analysis, contingency tables. Credit will not be
allowed for more than one of STAT 301, 302 or 303. Prerequisite: MATH 141 or
166 or equivalent.
307.
Sample Survey Techniques. (30). Credit 3. Concepts of population and sample; the
organization of a sample survey; questionnaire design. Basic survey designs and
computation of estimates and variances. Prerequisites: STAT 301, 302, 303, or
INFO 303.
407.
Principles of Sample Surveys. (30). Credit 3. Principles of sample surveys and survey
design; techniques for variance reduction; simple, stratified and multistage
sampling; ratio and regression estimates; poststratification; equal and unequal
probability sample. Prerequisite: STAT 212.
408.
Introduction to Linear Models. (30). Credit 3. Introduction to the
formulation of linear models and the estimation of the parameters of such
models, with primary emphasis on least squares. Application to multiple
regression and curve fitting. Prerequisites: MATH 304; STAT 212.
414.
Mathematical Statistics. (30). Credit 3. Introduction
to the mathematical theory of statistics, including random variables and their
distributions, expectation and variance, point estimation, confidence intervals
and hypothesis testing. Prerequisite: MATH 221, 251 or 253.
415.
Mathematical Statistics II. (30). Credit 3. Continuation
of the mathematical theory of statistics, including sampling and limiting
distributions, principles for statistical inference and inference for bivariate
and categorical data. Prerequisite: STAT 414.
485.
Problems. Credit 1 to 6. Special problems in statistics not covered by another
course in the curriculum. Work may be in either theory or methodology. Prerequisite:
Approval of instructor.
489.
Special Topics in Statistics. Credit 1 to 4. Selected topics in an identified area
of statistics. Topics may be of interest to applied mathematics majors as well
as majors in other disciplines. May be repeated for credit. Prerequisite:
Approval of instructor.
601.
Statistical Analysis. (32). Credit 4. For students
in engineering, physical, and mathematical sciences. Introduction to
probability, probability distributions, and statistical inference; hypotheses
testing; introduction to methods of analysis such as tests of independence,
regression, analysis of variance with some consideration of planned experimentation.
Prerequisite: MATH 152 or 172.
604.
Topics in Statistical Computations. (30). Credit 3. Efficient uses of existing statistical
computer programs (SAS, R, etc.), generation of random numbers; using and
creating functions and subroutines; statistical graphics; programming of
simulations studies; and data management issues. Prerequisites: MATH 221, 251,
or 253.
605.
Advanced Statistical Computations. (30). Credit 3. Programming languages,
statistical software, and computing environments; development of programming
skills using modern methodologies; data extraction
and code management; interfacing lowerlevel languages with data analysis software; simulation; MC integration;
MCMC procedures; permutation tests; bootstrapping. Prerequisite: STAT 612 and
STAT 648.
607.
Sampling. (30). Credit 3. Planning, execution and analysis of sampling from finite
populations; simple, stratified, multistage and systematic sampling; ratio
estimates. Prerequisite: STAT 601 or 652 or concurrent enrollment in STAT 641.
608.
Regression Analysis. (30). Credit 3. Multiple, curvilinear, nonlinear, robust,
logistic and principal components regression analysis; regression diagnostics, transformations,
analysis of covariance. Prerequisite: STAT 601 or 641.
610.
Theory of Statistics – Distribution Theory. (30). Credit 3. Brief introduction to
probability theory; distributions and expectations of random variables,
transformations of random variables, and order statistics; generating functions
and basic limit concepts. Prerequisite: MATH 409 or concurrent enrollment in
MATH 409.
611.
Theory of Statistics – Inference. (30). Credit 3. Theory of estimation
and hypothesis testing; point estimation, interval estimation, sufficient
statistics, decision theory, most powerful tests,
likelihood ratio tests, chisquare tests. Prerequisite: STAT 610 or equivalent.
612.
Theory of Linear Models. (30). Credit 3. Matrix algebra for statisticians,
GaussMarkov theorem; estimability; estimation subject to linear restrictions;
multivariate normal distribution; distribution of quadratic forms; inferences
for linear models; theory of multiple regression and AOV; random and
mixedeffects models. Prerequisite: Course in linear algebra.
613.
Statistical Methodology I. (30). Credit 3. Elements of likelihood inference;
exponential family models; group
transformation models; survival data; missing data; estimation and hypothesis
testing; nonlinear regression models; conditional and marginal inferences;
complex modelsMarkov chains, Markov random fields, time series, and point processes.
Prerequisite: STAT 612.
614.
Probability for Statistics. (30). Credit 3. Probability and measures; expectation
and integrals, Kolmogorov’s extension theorem; Fubini’s theorem; inequalities;
uniform integrability; conditional expectation; laws of large numbers; central
limit theorems. Prerequisite: STAT 610 or its equivalent.
615.
Stochastic Processes. (30). Credit 3. Survey of the theory of Poisson processes,
discrete and continuous time Markov chains, renewal processes, birth and death processes,
diffusion processes, and covariance stationary processes. Prerequisites: STAT
611; MATH 409.
616.
Multivariate Analysis. (30). Credit 3. Multivariate normal distributions and
multivariate generalizations of classical test criteria, Hotelling’s T^{2},
discriminant analysis and elements of factor and canonical analysis.
Prerequisites: STAT 611 and 612.
618. Statistical Aspects of Machine Learning and Data Mining.
(30). Credit 3.
This course will examine the statistical aspects of techniques used to examine
data streams which are large scale, dynamic, and heterogeneous. This course
will examine the underlying statistical properties of classification; trees;
bagging and boosting methods; neural networks; support vector machines; cluster
analysis; and independent component analysis. Prerequisites: STAT 610, 611, and
613.
620.
Asymptotic Statistics. (30). Credit 3. Review of basic concepts and important
convergence theorems; elements of decision theory; delta method; Bahadur
representation theorem; asymptotic distribution of MLE and the LRT statistics;
asymptotic efficiency; limit theory for Ustatistics and differential
statistical functionals with illustration from M,L,Restimation; multiple
testing. Prerequisite: STAT 614.
621.
Advanced Stochastic Processes. (30). Credit 3. Conditional
expectation; stopping times; discrete Markov processes; birthdeath processes; queuing
models; discrete semiMarkov processes; Brownian motion; diffusion processes,
Ito integrals, theorem and limit distributions; differential statistical functions
and their limit distributions; M,L,Restimation. Prerequisite: STAT 614 or
STAT 615.
623.
Statistical Methods for Chemistry. (30). Credit 3. Chemometrics topics of
process optimization, precision and accuracy; curve fitting; chisquared tests;
multivariate calibration; errors in calibration standards; statistics of instrumentation.
Prerequisites: STAT 601 or STAT 652 or STAT 641 or approval of instructor.
626.
Methods in Time Series Analysis. (30). Credit 3. Introduction to
statistical time series analysis; autocorrelation and spectral characteristics
of univariate, autoregressive, moving average models; identification, estimation
and forecasting. Prerequisite: STAT 601 or 642 or approval of instructor.
627.
Nonparametric Function Estimation. (30). Credit 3. Nonparametric function
estimation; kernel, local polynomials, Fourier series and spline methods;
automated smoothing methods including crossvalidation; large sample
distributional properties of estimators; recent advances in function
estimation. Prerequisites: STAT 611.
630.
Overview of Mathematical Statistics. (30). Credit 3. Basic probability
theory including distributions of random variables and expectations.
Introduction to the theory of statistical inference from the likelihood point
of view including maximum likelihood estimation, confidence intervals, and
likelihood ratio tests. Introduction to Bayesian methods. Prerequisites: Math
221, 251, or 253.
631.
Statistical Methods in Finance. (30). Credit 3. Regression and the
capital asset pricing model, statistics for portfolio analysis, resampling,
time series models, volatility models, option pricing and Monte Carlo methods,
copulas, extreme value theory, value at risk, spline smoothing of term
structure. Prerequisites: STAT 610, 611, 608.
632. Statistical
Methodology IIBayesian Modeling and Inference. (30). Credit 3. Decision theory;
fundamentals of Bayesian inference; single and multiparameter models, Gaussian
model; linear
and generalized linear models; Bayesian computation; asymptotic methods;
noninterative MC; MCMC; hierarchical models; nonlinear models; random effect
models; survival analysis; spatial models. Prerequisite: STAT 613.
633. Advanced Bayesian Modeling and Computation. (30). Credit
3. Bayesian
methods in their research; methodology, and applications of Bayesian methods in
bioinformatics, biostatistics, signal processing, machine learning, and related
fields. Prerequisite: STAT 608, 613, 632.
636.
Methods in Multivariate Analysis. (30). Credit 3. Multivariate
extensions of the chisquare and ttests, discrimination and classification
procedures; applications to diagnostic problems in biological, medical, anthropological,
and social research; multivariate analysis of variance, principal component and
factor analysis, canonical correlations. Prerequisites: MATH 423 and STAT 653
or approval of instructor. Crosslisted with INFO 657.
638. Introduction to Applied Bayesian
Methods. (30). Credit 3. Students will learn how uncertainty regarding
parameters can be explicitly described as a posterior distribution which blends
information from a sampling model and prior distribution Course will emphasize
modeling and computations under the Bayesian paradigm. Topics include: prior
distributions, Bayes Theorem, conjugate and nonconjugate models, posterior
simulation via the Gibbs sampler and MCMC, hierarchical modeling. Prerequisite:
STAT 604, 608, or 630.
641. The
Methods of Statistics I. (30). Credit 3. An application of the various
disciplines in statistics to data analysis, introduction to statistical
software; demonstration of interplay between probability models and statistical
inference. Prerequisites: Concurrent Enrollment in STAT 610 or approval of
instructor.
642. The
Methods of Statistics II (30). Credit 3. Design and analysis of experiments;
scientific method; graphical displays; analysis of nonconventional designs and experiments
involving categorical data. Prerequisites: STAT 641.
643.
Biostatistics I. (30). Credit 3. Bioassay for quantitative and quantal
responses; statistical analysis of contingency, including effect estimates,
matched samples and misclassification. Prerequisites: STAT 608, 630, and 642 or
STAT 610.
644.
Biostatistics II. (30). Credit 3. Generalized linear models; survival analysis
with emphasis on nonparametric models and methods. Prerequisites: STAT 643 or
approval of instructor.
645.
Applied Biostatistics and Data Analysis. (30). Credit 3. Survey of crucial topics in biostatistics; application of regression in
biostatistics; analysis of correlated data; logistic and Poisson regression for
binary or count data; survival analysis for censored outcomes; design and
analysis of clinical trials; sample size calculation by simulation; bootstrap
techniques for assessing statistical significance; data analysis using R. Prerequisites:
STAT 651, 652, and 659, or equivalent or prior approval of instructor.
646.
Statistical Bioinformatics. (30). Credit 3. An overview
of relevant biological concepts and technologies of genomic/proteomic
applications; methods to handle, visualize, analyze, and interpret
genomic/proteomic data; exploratory data analysis for genomic/ proteomic data;
data preprocessing and normalization; hypotheses testing; classification and
prediction techniques for using genomic/ proteomic data to predict disease status.
Prerequisites: STAT 604, 651, 652 or equivalent or prior approval of
instructor.
647.
Spatial Statistics. (30). Credit 3. Spatial correlation and its effects; spatial
prediction (kriging); spatial regression; analysis of point patterns (tests for
randomness and modelling patterns); sub sampling methods for spatial data. Prerequisite:
STAT 601 or STAT 611 or equivalent.
648.
Applied Statistics and Data Analysis. (30). Credit 3. Background to conduct
research in the development of new methodology in applied statistics. Topics
covered will include: exploratory data analysis; sampling; testing; smoothing; classification;
time series; and spatial data analysis. Prerequisite: Approval of instructor.
651.
Statistics in Research I. (30). Credit 3. For graduate students in other
disciplines; noncalculus exposition of the concepts, methods and usage of
statistical data analysis; Ttests, analysis of variance, and linear regression.
Prerequisite: MATH 102 or equivalent.
652.
Statistics in Research II. (30). Credit 3. Continuation of STAT 651. Concepts of
experimental design, individual treatment comparisons, randomized blocks and factorial
experiments, multiple regression, chisquare tests and a brief introduction to
covariance, nonparametric methods, and sample surveys. Prerequisite: STAT 651.
653.
Statistics in Research III. (30). Credit 3. Advanced topics in ANOVA; analysis of
covariance; and regression analysis including analysis of messy data;
nonlinear regression; logistic and weighted regression, diagnostics and model
building; emphasis on concepts, computing and interpretation. Prerequisite:
STAT 652.
656.
Applied Analytics Using SAS Enterprise Miner. (30) Credit 3. Introduction to data mining and will demonstrate the procedures; Optimal
prediction decisions; comparing and deploying predictive models; neural
networks; constructing and adjusting tree models; the construction and
evaluation of multistage models. Prerequisite: STAT 657.
657.
Advanced Programming Using SAS. (30). Credit 3. Programming with
SAS/IML, programming in SAS data step, advanced use of various SAS procedures.
Prerequisites: STAT 604, 642.
658.
Transportation Statistics. (30). Credit 3. Design of experiments, estimation,
hypotheses testing, modeling, and data mining for transportation specialists.
Prerequisites: STAT 211 or STAT 651.
659.
Applied Categorical Data Analysis. (30). Credit 3. Introduction to
analysis and interpretation of categorical data using ANOVA/regression analogs;
includes contingency tables, loglinear models, logistic regression; use of
computer software such as SAS, GLIM, SPSSX. Prerequisite: STAT 601 or 641 or
652 or equivalent.
661.
Statistical Genetics. (30), Credit 3. Basic concepts in human genetics, sampling
designs, gene frequency estimation, HardyWeinberg equilibrium, linkage
disequilibrium, association and transmission disequilibrium test studies, linkage
and pedigree analysis, segregation analysis, polygenic models, DNA sequence
analysis. Prerequisites: STAT 610 and 611.
662.
Advanced Statistical Genetics. (30). Credit 3. This course is a
continuation of the course, STAT 661 Statistical Genetics. A strong background
in statistics, genetics, and mathematics is required. Topics include counting
methods, EM algorithm, Newton’s method, scoring in genetics, genetic identity
coefficients, descent graph, molecular phylogeny, models of recombination,
sequence analysis, diffusion processes and linkage disequilibrium mappings.
Prerequisite: STAT 610, 611, and 661.
665. Statistical
Application of Wavelets. (30). Credit 3. This is a course on the use of wavelets
methods in statistics. The course introduces wavelet theory, provides an
overview of waveletbased statistical methods. Topics include smoothing of
noisy signals, estimation function data and representation of stochastic
processes. Some emphasis is given to Bayesian procedures. Prerequisite: STAT
611 or approval by the instructor.
673. Time Series Analysis I. (30). Credit 3. An introduction to diverse modes of analysis now available to
solve for univariate time series; basic problems of parameter estimation,
spectral analysis, forecasting and model identification. Prerequisite: STAT 611
or equivalent.
674.
Time Series Analysis II. (30). Credit 3. Continuation of STAT 673. Multiple time
series, ARMA models, test of hypotheses, estimation of spectral density matrix,
transfer function and forecasting. Prerequisites: STAT 673.
681.
Seminar. Credit 1.
Oral presentations of special topics and current research in statistics. May be
repeated for credit. Prerequisite: Graduate classification in statistics.
684. Professional
Internship. Credit 1 to 3. Practicum in statistical consulting for students in Ph.D program.
Students will be assigned consulting problems brought to the Department of
Statistics by researchers in other disciplines. Prerequisite: STAT 642 or
equivalent.
685. Directed
Studies. Credit 1 to 6. Individual instruction in selected fields in statistics;
investigation of special topics not within scope of thesis research and not
covered by other formal courses. Prerequisites: Graduate classification and
approval of department head.
689.
Special Topics in Statistics. Credit 1 to 4. Selected topics in an identified area
of statistics. Open to nonmajors. May be repeated for credit. Prerequisite:
Approval of instructor.
691.
Research. Credit 1 or more. Research for thesis or dissertation. Prerequisite:
Graduate classification.
The following list
indicates the Department’s usual schedule of course offerings. Those courses
marked even or odd are offered only in even numbered and odd numbered years,
respectively. Because several courses are offered only every other year, it is important
to plan a program of study and schedule of courses as early as possible.
Course 
Semester(s) Offered 

Course 
Semester(s) Offered 
201 
1,2 

631 
2 (odd) 
211 
1,2,3 

632 
1 
212 
1,2 

633 
2 
301 
1,2 

636 
1 
302 
1,2,3 

638 
1 
303 
1,2,3 

641 
1 
307 
1,2 

642 
2 
407 
1 

643 
1 (odd) 
408 
2 

644 
2 (even) 
414 
1 

645 
1 
485 
1,2,3 

646 
2 
601 
1 

647 
1 
604 
1,2,3 

648 
1 
605 
2 

651 
1,2,3 
607 
1 

652 
1,2,3 
608 
2,3 

653 
2 
610 
1 

656 
2 
611 
2 

657 
2 
612 
1 

658 
4 
613 
2 

659 
2,3 
614 
1 

661 
1 
615 
1 

662 
2 (odd) 
616 
2 

665 
4 
618 
1 

673 
1 (even) 
620 
2 

674 
2 (odd) 
621 
2 

681 
1,2 
623 
4 

684 
1,2,3 
626 
3 

685 
1,2,3 
627 
2 (odd) 

691 
1,2,3 
630 
1,2,3 



1: Fall, 2: Spring, 3: Summer, 4: As
resources allow.