Undergraduate Courses
STAT 111 Lectures in
Applied Statistics (1)
Weekly lectures illustrating the
importance of statisticians in a variety of fields, including medicine and
the biological, physical, and social sciences. Contact instructor for
information on emphasized fields of applications. Credit/no credit only.
Offered: jointly with BIOST 111; Sp.
STAT 220 Basic Statistics
(5)
Objectives and pitfalls of statistical studies.
Structure of data sets, histograms, means, and standard deviations.
Correlation and regression. Probability, binomial and normal.
Interpretation of estimates, confidence intervals, and significance tests.
(Students may receive credit for only one of 220, 301, 311, and ECON 311.)
Offered: AWSpS.
STAT 301 Basic Statistics
with Applications (5)
Objectives, pitfalls of
statistical studies. Structure of data sets, histograms, means, standard
deviations. Correlation, regression. Probability, binomial and normal.
Interpretation of estimates, confidence intervals, significance tests.
Application to problems in student's major field. (Students may receive
credit for only one of 220, 301, 311, and ECON 311.) Offered: Sp.
STAT 311 Elements of
Statistical Methods (5)
Elementary concepts of
probability and sampling; binomial and normal distributions. Basic
concepts of hypothesis testing, estimation, and confidence intervals;
t-tests and chi-square tests. Linear regression theory and the analysis of
variance. (Students may receive credit for only one of 220, 301, 311, and
ECON 311.) Prerequisite: either MATH 111, MATH 120, MATH 124, MATH 127,
or MATH 144. Offered: AWSpS.
STAT 316 Regression
Analysis and Design of Experiments (3)
Introduction to the
analysis of data from planned experiments. Analysis of variance and
regression analysis with applications in engineering. Prerequisite:
IND E 315. Offered: jointly with IND E 316.
STAT 320 Evaluating Social
Science Evidence (5) I&S
A critical introduction to the methods
used to collect data in social science: surveys, archival research,
experiments, and participant observation. We teach you to evaluate "facts
and findings" by understanding the strengths and weaknesses of the
methods that produce them. Case based, no prerequisites. Offered:
jointly with SOC 320 and CS&SS 320.
STAT 321 Social Statistics
Case-Based I (5)
Introduction to statistical reasoning for
social scientists. Built around cases representing in-depth
investigations into the nature and content of statistical and
social-science principles and practice. Hands-on approach: weekly
data-analysis laboratory. Fundamental statistical topics: measurement,
exploratory data analysis, probabilistic concepts, distributions,
assessment of statistical evidence. Offered: jointly with CS&SS/SOC 321; W.
STAT 322 Social Statistics
Case-Based II (5)
Continuation of CS&SS/SOC/STAT
321. Progresses to questions of assessing the weight of evidence and
more sophisticated models including regression-based methods. Built
around cases investigating the nature and content of statistical
principles and practice. Hands-on approach: weekly data analysis
laboratory. Offered: jointly with CS&SS/SOC 322;
Sp.
STAT 341 Introduction to
Probability and Statistical Inference I (4)
Brief review
of: sample spaces, random variable, probability. Distribution: binomial,
normal, Poisson, geometric. Followed by: expectation, variance, central
limit theorem. Basic concepts of estimation, testing, and confidence
intervals. Maximum likelihood estimators and likelihood ratio tests,
efficiency. Introduction to regression. Prerequisite: STAT/ECON 311;
either MATH 126, MATH 129, or MATH 136; STAT/MATH 394. Offered: W.
STAT 342 Introduction to
Probability and Statistical Inference II (4)
Brief review
of: sample spaces, random variable, probability. Distribution: binomial,
normal, Poisson, geometric. Followed by: expectation, variance, central
limit theorem. Basic concepts of estimation, testing, and confidence
intervals. Maximum likelihood estimators and likelihood ratio tests,
efficiency. Introduction to regression. Prerequisite: 341. Offered:
Sp.
STAT 361 Statistics for
Social Scientists (3)
Introduction to statistical
methodology, measurement scales, design of surveys and experiments,
descriptive statistics, exploratory data analysis, probability
distributions, use of computer packages for statistical data analysis,
point and interval estimation hypothesis testing. Comparisons, two sample
tests, nonparametric methods, measuring and testing association,
correlation, simple linear and multiple regression, time series,
multivariate data analysis, analysis of variance (ANOVA) and analysis and
covariance (ANCOVA). Computers used, but no prior experience required.
Prerequisite: STAT/ECON 311 or 220. Not currently offered.
STAT 362 Statistics for
Social Scientists (3)
Introduction to statistical
methodology, measurement scales, design of surveys and experiments,
descriptive statistics, exploratory data analysis, probability
distributions, use of computer packages for statistical data analysis,
point and interval estimation hypothesis testing. Comparisons, two sample
tests, nonparametric methods, measuring and testing association,
correlation, simple linear and multiple regression, time series,
multivariate data analysis, analysis of variance (ANOVA) and analysis and
covariance (ANCOVA). Computers used, but no prior experience required.
Prerequisite: 361. Not currently offered.
STAT 390 Probability and
Statistics in Engineering and Science (4)
Concepts of
probability and statistics. Conditional probability, independence, random
variables, distribution functions. Descriptive statistics,
transformations, sampling errors, confidence intervals, least squares and
maximum likelihood. Exploratory data analysis and interactive computing.
Students may receive credit for only one of 390, STAT/ECON 481, and ECON
580. Prerequisite: either MATH 126 or MATH 136. Offered: jointly with MATH 390; AWSpS.
STAT 391 Probability and
Statistics for Computer Science (4)
Fundamentals of probability and statistics from the perspective of the
computer scientist.
Random variables, distributions and densities, conditional probability,
independence.
Maximum likelihood, density estimation, Markov chains, classification.
Applications in computer science. Prerequisite: 2.5 in MATH 126; 2.5 in
MATH 308; either CSE 326, CSE 373, CSE 417, or CSE 421
STAT 394 Probability I
(3)
Sample spaces; basic axioms of probability;
combinatorial probability; conditional probability and independence;
binomial, Poisson and normal distributions. Prerequisite: either 2.0 in
MATH 126, 2.0 in MATH 129, or 2.0 in MATH 136; recommended: MATH 324 or MATH 327. Offered:
jointly with MATH 394; AWS.
STAT 395 Probability II
(3)
Random variables; expectation and variance; laws of
large numbers; normal approximation and other limit theorems;
multidimensional distributions and transformations. Prerequisite:
STAT/MATH 394. Offered: jointly with MATH 395; WSpS.
STAT 396 Probability III
(3)
Characteristic functions and generating functions;
recurrent events and renewal theory; random walk. Prerequisite: either 2.0
in MATH 395 or 2.0 in 395. Offered: jointly with MATH 396; Sp.
STAT 400 Mathematical
Communication for Undergraduates (2)
Techniques of effective writing
and oral presentations in the mathematical sciences. Offered: jointly with
AMATH 400/MATH 400. Prerequisite: at least 15 credits in MATH, STAT, AMATH,
or CSCI at the 300 or 400 level, including MATH 307 or AMATH 351 and MATH 308
or AMATH 352.
STAT 403 Introduction to
Resampling Inference (4)
Introduction to
computer-intensive data analysis for experimental and observational
studies in empirical sciences. Students design, program, carry out, and
report applications of bootstrap resampling, rerandomization, and
subsampling of cases. Credit allowed for 403 or 503 but not both.
Prerequisite: either 220, 301, STAT/ECON 311, 341, STAT
361, STAT/MATH 390, or STAT/ECON 481. Offered: Sp.
STAT 421 Applied
Statistics and Experimental Design (4)
Computer-aided data
analyses using comparisons between batches, analysis of variance and
regression. Evaluation of assumptions, data transformation, reliability of
statistical measures (jackknife, bootstrap). Fisher-Gosset controversy.
Prerequisite: either 342, STAT/MATH 390, or STAT/ECON 481;
recommended: MATH 308. Offered: A.
STAT 423 Applied
Regression and Analysis of Variance (4)
Regression
analysis. Problems in interpreting regression coefficients. Estimation,
including two-stage least squares. Guided regression: building linear
models, selecting carriers. Regression residuals. Analysis of variance.
Nonparametric regression. Factorial designs, response surface methods.
Prerequisite: either 342, STAT/MATH 390, 421, or STAT/ECON 481;
recommended: MATH 308. Offered: W.
STAT 425 Introduction to
Nonparametric Statistics (3)
Overview of nonparametric
methods, such as rank tests, goodness of fit tests, 2 x 2 tables,
nonparametric estimation. Useful for students with only a statistical
methods course background. Prerequisite: STAT/MATH 390. Offered: jointly
with BIOST 425; when demand is sufficient.
STAT 427 Introduction to
Analysis of Categorical Data (4)
Techniques for analysis
of count data. Log-linear models, logistic regression, and analysis of
ordered response categories. Illustrations from the behavioral and
biological sciences. Computational procedures. Prerequisite: either STAT
342, 362, or 421. Offered: alternate years.
STAT 428 Multivariate
Analysis for the Social Sciences (4)
Multivariate
techniques commonly used in the social and behavioral sciences. Linear
models for dependence analysis (multivariate regression, MANOVA, and
discriminant analysis) and for interdependence analysis (principal
components and factor analysis). Techniques applied to social science data
using computer statistical packages. Prerequisite: either 342, STAT
362, or 421. Offered: alternate years.
STAT 480 Sampling Theory
for Biologists (3)
Theory and
applications of sampling finite populations including: simple random
sampling, stratified random sampling, ratio estimates, regression
estimates, systematic sampling, cluster sampling, sample size
determinations, applications in fisheries and forestry. Other topics
include sampling plant and animal populations, sampling distributions,
estimation of parameters and statistical treatment of data. Prerequisite:
Q SCI 482; recommended: Q SCI 483. Offered: jointly with Q SCI 480; even
years.
STAT 481 Introduction to
Mathematical Statistics (5)
NW Probability, generating
functions; the d-method, Jacobians, Bayes theorem; maximum likelihoods,
Neyman-Pearson, efficiency, decision theory, regression, correlation,
bivariate normal. (Credit allowed for only one of 390, 481, and ECON 580.)
Prerequisite: STAT/ECON 311; either MATH 136 or MATH 126 with either MATH
308 or MATH 309. Recommended: MATH 324.
Offered: jointly with CSSS/ECON 481; A.
STAT 486 Experimental
Design (3)
Topics in analysis of variance and experimental
designs: choice of designs, comparison of efficiency, power, sample size,
pseudoreplication, factor structure. Prerequisite: Q SCI 482; recommended:
Q SCI 483. Offered: jointly with Q SCI 486.
STAT 491 Introduction to
Stochastic Processes (3)
Random walks, Markov chains,
branching processes, Poisson process, point processes, birth and death
processes, queuing theory, stationary processes. Prerequisite: 2.0 in
MATH/STAT 396. Offered: jointly with MATH 491; A.
STAT 492 Introduction to
Stochastic Processes (3)
Random walks, Markov chains,
branching processes, Poisson process, point processes, birth and death
processes, queuing theory, stationary processes. Prerequisite: 2.0 in
MATH/STAT 491. Offered: jointly with MATH 492; W.
STAT 498 Special Topics
(1-5, max. 15)
Reading and lecture course intended for
special needs of students. Offered: When demand is sufficient.
STAT 499 Undergraduate
Research (1-5)
Offered: AWSpS.
STAT 500 Mathematical Communication for Graduates (2)
Analysis and practice of mathematical writing. Oral and poster conference presentations. Academic job interview skills. Mathematics on the web. Offered: jointly with AMATH 580/MATH 500.
STAT 504 Applied Regression
(4)
Least squares estimation. Hypothesis
testing. Interpretation of regression coefficients. Categorical
independent variables. Interactions. Assumption violations: outliers,
residuals, robust regression; nonlinearity, transformations, ACE, CART;
nonconstant variance. Variable selection and model
averaging. Prerequisite: either 342, 421, STAT/MATH 390,
STAT/ECON 481, or SOC 425; recommended: MATH 308. Offered jointly with
CS&SS 504.
STAT 506 Applied
Probability and Statistics (4)
Discrete and continuous random
variables, independence and conditional probability, central limit theorem, elementary statistical estimation and inference, linear regression. Emphasis on physical applications.
Prerequisite: some advanced calculus and linear
algebra. Offered:
jointly with AMATH 506.
STAT 512 Statistical
Inference (4)
Review of random variables; transformations,
conditional expectation, moment generating functions, convergence, limit
theorems, estimation; Crame'r-Rao lower bound, maximum likelihood
estimation, sufficiency, ancillarity, completeness, Rao-Blackwell theorem.
Hypothesis testing: size, power, Neyman-Pearson lemma, monotone likelihood
ratio, likelihood-ratio tests, large-sample theory. Contingency tables.
Confidence intervals. Invariance. Introduction to decision
theory. Prerequisite: 395 and 421, 423, or BIOST 512 (concurrent
registration permitted for these three). Offered: A.
STAT 513 Statistical
Inference (4)
Review of random variables; transformations,
conditional expectation, moment generating functions, convergence, limit
theorems, estimation; Crame'r-Rao lower bound, maximum likelihood
estimation, sufficiency, ancillarity, completeness, Rao-Blackwell theorem.
Hypothesis testing: size, power, Neyman-Pearson lemma, monotone likelihood
ratio, likelihood-ratio tests, large-sample theory. Contingency tables.
Confidence intervals. Invariance. Introduction to decision
theory. Prerequisite: 512. Offered: W.
STAT 516 Stochastic
Modeling of Scientific Data (4)
Markovian and semi-Markovian
models, point processes, cluster models, queuing models, likelihood
methods, estimating equations. Prerequisite: 396. Offered: A.
STAT 517 Stochastic
Modeling of Scientific Data (4)
Markovian and semi-Markovian
models, point processes, cluster models, queuing models, likelihood
methods, estimating equations. Prerequisite: 516. Offered: W.
STAT 518 Stochastic
Modeling Project (4)
Supervised, applied project based on
stochastic modeling of scientific data. Prerequisite: 517. Offered:
Sp.
STAT 519 Time Series
Analysis (3)
Descriptive techniques. Stationary and
nonstationary processes, including ARIMA processes. Estimation of process
mean and autocovariance function. Fitting ARIMA models to data.
Statistical tests for white noise. Forecasting. State space models and the
Kalman filter. Robust time series analysis. Regression analysis with
correlated errors. Statistical properties of long memory processes.
Prerequisite: 513. Offered: A.
STAT 520 Spectral
Analysis of Time Series (4)
Estimation of spectral densities
for single and multiple time series. Nonparametric estimation of spectral
density, cross-spectral density, and coherency for stationary time series,
real and complex spectrum techniques. Bispectrum. Digital filtering
techniques. Aliasing, prewhitening. Choice of lag windows and data
windows. Use of the fast Fourier transform. The parametric autoregressive
spectral density estimate for single and multiple stationary time series.
Spectral analysis of nonstationary random processes and for randomly
sampled processes. Techniques of robust spectral analysis. Prerequisite:
one of 342, 390, 481, or permission of instructor. Offered: jointly with E
E 520; W.
STAT 521 Advanced
Probability (3)
Measure theory and integration, independence,
laws of large numbers. Fourier analysis of distributions, central limit
problem and infinitely divisible laws, conditional expectations,
martingales. Prerequisite: MATH 426. Offered: jointly with MATH 521;
A.
STAT 522 Advanced
Probability (3)
Measure theory and integration, independence,
laws of large numbers. Fourier analysis of distributions, central limit
problem and infinitely divisible laws, conditional expectations,
martingales. Prerequisite: 521 and MATH 426. Offered: jointly with MATH
522; W.
STAT 523 Advanced
Probability (3)
Measure theory and integration, independence,
laws of large numbers. Fourier analysis of distributions, central limit
problem and infinitely divisible laws, conditional expectations,
martingales. Prerequisite: 522 and MATH 426. Offered: jointly with MATH
523; Sp.
STAT 524 Design of Medical
Studies (3)
Emphasis on randomized controlled clinical
trials. Bias elimination, controls, treatment assignment and
randomization, precision, replication, power and sample size
calculations, stratification, and ethics. Suitable for students in
biostatistics and other scientific fields. Prerequisite: BIOST 511 or
equivalent, and one of 421, 423, BIOST 513 or EPI 512; or permission of
instructor. Offered: jointly with BIOST 524; even years.
STAT 529 Sample Survey
Techniques (3)
Design and implementation of selection and
estimation procedures. Emphasis on human populations. Simple,
stratified, and cluster sampling; multistage and two-phase procedures;
optimal allocation of resources; estimation theory; replicated designs;
variance estimation; national samples and census
materials. Prerequisite: 421, 423, QMETH 500 or BIOST 511 or equivalent;
or permission of instructor. Offered: jointly with BIOST 529.
STAT 530 Wavelets: Data
Analysis, Algorithms, and Theory (3)
Review of spectral
analysis. Theory of continuous and discrete wavelets. Multiresolution
analysis. Computation of discrete wavelet transform. Time-scale
analysis. Wavelet packets. Statistical properties of wavelet signal
extraction and smoothers. Estimation of wavelet variance. Prerequisite:
some Fourier theory and linear algebra; 390, 481, or 513; or permission
of instructor. Offered: Sp.
STAT 533 Classical Theory of
Linear Models (3)
Introduction to one-, two-way analysis of
variance; randomized blocks; fixed, random effects, multiple
comparisons. Statistical distribution theory for quadratic forms of
normal variables. Fitting of the general linear model by least squares.
Prerequisite: 421 or 423; and 513, BIOST 513, and a course in matrix
algebra. Offered: jointly with BIOST 533; Sp.
STAT 534 Statistical
Computing (3)
Introduction to scientific computing. Includes
programming tools, modern programming methodologies, (modularization,
object oriented design), design of data structures and algorithms,
numerical computing and graphics. Uses C++ for several substantial
scientific programming projects. Prerequisite: experience with
programming in a high level language. Offered: jointly with BIOST 534;
Sp.
STAT 535 Statistical
Computing (3)
Introduction to scientific computing. Includes
programming tools, modern programming methodologies, (modularization,
object oriented design), design of data structures and algorithms,
numerical computing and graphics. Uses C++ for several substantial
scientific programming projects. Prerequisite: experience with
programming in a high level language. Offered: jointly with BIOST 535;
A.
STAT 536 Log-Linear Modeling
and Logistic Regression for the Social Sciences (3)
Log-linear
modeling of multidimensional contingency tables. Logistic
regression. Applications to social mobility, educational opportunity,
and assortative marriage. Applied and computing focus. Prerequisite:
395 or SOC 425 or permission of instructor. Offered: jointly with SOC
536/CS&SS 536.
STAT 538 Statistical
Computing (3)
Introduction to scientific computing. Includes
programming tools, modern programming methodologies, (modularization,
object oriented design), design of data structures and algorithms,
numerical computing and graphics. Uses C++ for several substantial
scientific programming projects. Prerequisite: experience with
programming in a high level language. Offered: jointly with BIOST 538;
W.
STAT 542 Multivariate
Analysis (3)
Multivariate normal distribution; partial and
multiple correlation; Hotelling's T2; Bartlett's
decomposition; various likelihood ratio tests; discriminant analysis;
principal components; graphical Markov models. Prerequisite: 582 or
permission of instructor. Offered: alternate years.
STAT 544 Bayesian
Statistical Methods (3)
Statistical methods based on the idea of
a probability distribution over the parameter space. Coherence and
utility. Subjective probability. Likelihood principle. Conjugate
families. Structure of Bayesian inference. Limit theory for posterior
distributions. Sequential experiments. Exchangeability. Bayesian
nonparametrics. Empirical Bayes methods. Prerequisite: 513 or
permission of instructor. Offered: alternate years.
STAT 550 Statistical
Genetics I: Mendelian Traits (3)
Mendelian genetic
traits. Population genetics; Hardy-Weinberg, allelic variation,
subdivision. Likelihood inference, information and power; latent
variables and EM algorithm. Pedigree relationships and gene identity.
Meiosis and recombination. Linkage detection. Multipoint linkage
analysis. Prerequisite: 390 and 394, or permission of instructor.
Offered: jointly with BIOST 550; A.
STAT 551 Statistical
Genetics II: Quantitative Traits (3)
Statistical basis for
describing variation in quantitative traits. Decomposition of trait
variation into components representing genes, environment and
gene-environment interaction. Methods of mapping and characterizing
quantitative trait loci. Prerequisite: STAT/BIOST 550; STAT 423 or BIOST
515; or permission of instructor. Offered: jointly with BIOST 551; W.
STAT 552 Statistical
Genetics III: Medical Genetics Studies (3)
Overview of
probability models, inheritance models, penetrance. Association and
linkage. The lod score method. Affected sib method. Fitting complex
inheritance models. Design mapping studies; multipoint,
disequilibrium, and fine-scale mapping. Ascertainment. Prerequisite:
STAT/BIOST 551; GENET 371; or permission of instructor. Offered:
jointly with BIOST 552; Sp.
STAT 560 Hierarchical
Modeling for the Social Sciences (4)
Explores ways in which data
are hierarchically organized, such as voters nested within electoral
districts that are in turn nested within states. Provides a basic
theoretical understanding and practical knowledge of models for
clustered data and a set of tools to help make accurate
inferences. Offered: jointly with CS&SS/POL S 560.
STAT 564 Bayesian Statistics
for the Social Sciences (4)
Statistical methods based on the
idea of probability as a measure of uncertainty. Topics covered include
subjective notion of probability, Bayes' Theorem, prior and posterior
distributions, and data analysis techniques for statistical
models. Prerequisite: introductory statistics. Offered: jointly with
CS&SS 564.
STAT 566 Causal Modeling
(3-5)
Construction of causal hypotheses. Theories of causation,
counterfactuals, intervention vs. passive observation. Contexts for
causal inference: randomized experiments; sequential randomization;
partial compliance; natural experiments, passive observation. Path
diagrams, conditional independence and d-separation. Model equivalence
and causal under-determination. Prerequisite: course in
statistics. Offered: jointly with CS&SS 566.
STAT 567 Statistical
Analysis of Social Networks (4)
Statistical and mathematical
descriptions of social networks. Topics include graphical and matrix
representations of social networks, sampling methods, statistical
analysis of network data, and applications. Prerequisite: introductory
statistics. Offered: jointly with CS&SS 567.
STAT 570 Advanced Applied
Statistics and Linear Models (3)
Generalized linear models,
REML in mixed models for randomized blocks, split plots, longitudinal
data. Generalized estimating equations, empirical model building, cross
validation, recursive partitioning, generalized additive models,
projection pursuit. Prerequisite: 513; 533 or 421 and 423, and a course in
matrix algebra for 570. Offered: jointly with BIOST 570; A.
STAT 571 Advanced Applied
Statistics and Linear Models (3)
Generalized linear models,
REML in mixed models for randomized blocks, split plots, longitudinal
data. Generalized estimating equations, empirical model building, cross
validation, recursive partitioning, generalized additive models,
projection pursuit. Prerequisite: 570. Offered: jointly with BIOST 571;
W.
STAT 572 Advanced Applied
Statistics and Linear Models (3)
Generalized linear models,
REML in mixed models for randomized blocks, split plots, longitudinal
data. Generalized estimating equations, empirical model building, cross
validation, recursive partitioning, generalized additive models,
projection pursuit. Prerequisite: 571. Offered: jointly with BIOST 572;
Sp.
STAT 573 Statistical
Methods for Categorical Data (3)
Advanced topics in
generalized linear models and the analysis of categorical data:
overdispersion, quasilikelihood, parameters in link and variance
functions, exact conditional inference, random effects, saddlepoint
approximations. Credit/no credit only. Prerequisite: 571 and 582.
Offered: jointly with BIOST 573; alternate years.
STAT 574 Multivariate
Statistical Methods (3)
Use of multivariate normal sampling
theory, linear transformations of random variables, one- and two-sample
tests, profile analysis, partial and multiple correlation, multivariate
ANOVA and least squares, discriminant analysis, principal components,
factor analysis, robustness, and some special topics. Some computer use
included. Prerequisite: 570 or permission of instructor. Offered: jointly
with BIOST 574; alternate years.
STAT 576 Statistical
Methods for Survival Data (3)
Statistical methods for
censored survival data. Covers parametric and nonparametric methods,
Kaplan-Meier survival curve estimator, comparison of survival curves,
log-rank test, regression models including the Cox proportional hazards
model, competing risks. Prerequisite: 581 and either 423, BIOST 513, or Q
SCI 483, or equivalent. Offered: jointly with BIOST 576; alternate
years.
STAT 577 Advanced Design
and Analysis of Experiments (3)
Concepts important in
experimental design: randomization, blocking, confounding. Application and
analysis of data from randomized blocks designs, Latin and Greco-Latin
squares, incomplete blocks designs, split-plot and repeated measures,
factorial and fractional replicates, response surface experiments.
Prerequisite: 570 or 421 (minimum grade 3.0), or permission of instructor.
Offered: jointly with BIOST 577.
STAT 578 Special Topics
in Advanced Biostatistics (*)
Advanced-level topics in
biostatistics offered by regular and visiting faculty members.
Prerequisite: permission of instructor. Offered: jointly with BIOST
578.
STAT 581 Advanced Theory
of Statistical Inference (3)
Limit theorems, asymptotic
methods, asymptotic efficiency and efficiency bounds for estimation,
maximum likelihood estimation, Bayes methods, asymptotics via derivatives
of functionals, sample-based estimates of variability: (bootstrap and
jackknife); robustness; estimation for dependent data, nonparametric
estimation and testing. Prerequisite: 513 and MATH 426. Offered: A.
STAT 582 Advanced Theory
of Statistical Inference (3)
Limit theorems, asymptotic
methods, asymptotic efficiency and efficiency bounds for estimation,
maximum likelihood estimation, Bayes methods, asymptotics via derivatives
of functionals, sample-based estimates of variability: (bootstrap and
jackknife); robustness; estimation for dependent data, nonparametric
estimation and testing. Prerequisite: 581. Offered: W.
STAT 583 Advanced Theory
of Statistical Inference (3)
Limit theorems, asymptotic
methods, asymptotic efficiency and efficiency bounds for estimation,
maximum likelihood estimation, Bayes methods, asymptotics via derivatives
of functionals, sample-based estimates of variability: (bootstrap and
jackknife); robustness; estimation for dependent data, nonparametric
estimation and testing. Prerequisite: 582. Offered: Sp.
STAT 586 Martingales:
Survival Analysis (3)
Theory of counting
processes and martingales to provide unified study of survival analysis
methods. Focus on survival distribution estimators, censored data rank
statistics, regression methods with censored survival data. Development of
small samples moments, asymptotic distributions, and efficiencies.
Prerequisite: 521 or 583 or permission of instructor; recommended: 576. Offered: jointly with BIOST 586; W.
STAT 590 Statistics
Seminar (*)
Credit/no credit only. Prerequisite:
permission of graduate program coordinator. Offered: AWSp.
STAT 591-2-3 Special Topics
in Statistics (1-5)
Topics of current research interest. Recent offerings include:
Applications of Empirical Process Theory; Environmental Statistics;
Graphical Markov Models; Mathematical Communication;
Mieoses, Pedigrees and Populations; Spatial Statistics;
Applications of Empirical Process Theory. Offered: AWSp.
STAT 598 Techniques of
Statistical Consulting (1)
Seminar series covering technical and non-technical aspects of
statistical consulting, including skills for effective communication
with clients, report writing, statistical tips and rules of thumb,
issues in survey sampling, and issues in working as a statistical
consultant in academics, industrial, and private-practice
settings. Prerequisite: entry code. Offered: jointly with BIOST 598; ASp.
STAT 599 Statistical
Consulting (*)
Consulting experience in data
analysis, applied statistics. Student required to provide consulting
services to students and faculty three hours per week. Credit/no credit
only. Prerequisite: permission of graduate program coordinator. Offered:
AWSpS.
STAT 600 Independent
Study or Research (*)
Prerequisite: permission of graduate
program coordinator. Offered: AWSpS.
STAT 700 Master's Thesis
(*)
Prerequisite: permission of graduate program coordinator.
Offered: AWSpS.
STAT 800 Doctoral
Dissertation (*)
Prerequisite: permission of graduate program
coordinator. Offered: AWSpS.