Courses and Programs 1999-2001
Courses and Programs 1999-2001Courses Primarily for Undergraduate Students
Stat 100. Orientation in Statistics.
(1-0) Cr. R. F. Opportunities, challenges, and the scope of the
curriculum in statistics. For students planning or considering a
career in this area.
Stat 101. Principles of Statistics.
(3-2) Cr. 4. F.S.SS. Prereq: 1 1/2 years of high school algebra.
Statistical concepts in modern society; descriptive statistics
and graphical displays of data; the normal distribution; data
collection; elementary probability; elements of statistical
inference; estimation and hypothesis testing; linear regression
and correlation; contingency tables. Credit for only one of the
following courses may be applied toward graduation: 101, 104,
105, 227.
Stat 104. Introduction to Statistics.
(2-2) Cr. 3. F.S.SS. Prereq: 1 1/2 years of high school algebra.
Statistical concepts and their use in science; collecting,
organizing and drawing conclusions from data; sampling and
experimentation as ways of generating data; methods for
describing and summarizing data and understanding relationships;
statistical inference. For students in the agricultural and
biological sciences. Credit for only one of the following courses
may be applied toward graduation: 101, 104, 105, 227.
Stat 105. Introduction to Statistics
for Engineers.
(3-0) Cr. 3. F.S. Prereq: Math 165 (or 165H). Statistical
concepts with emphasis on engineering applications. Data
collection; descriptive statistics; probability distributions and
their properties; elements of statistical inference; regression;
statistical quality control charts; use of statistical software;
team project involving data collection, description and analysis.
Credit for only one of the following courses may be applied
toward graduation: 101, 104, 105, 227. Credit for both 105 and
305 may not be applied for graduation.
Stat 201. Applied Regression Analysis for
Business.
(2-0) Cr. 2. F. Prereq: 101 or 104 or 105. Brief review of
required descriptive and inferential statistics; statistical
process monitoring and applications in quality control; use of
computers to analyze data; simple linear regression analysis;
multiple regression analysis; diagnostic checking and model
building; application of regression techniques to analysis of
variance and time series analysis. Credit for both 201 and 227
may not be applied toward graduation.
Stat 227. Introduction to Business
Statistics.
(4-2) Cr. 5. F.S.SS. Prereq: Math 150 or 165. Obtaining,
presenting, and organizing statistical data; measures of location
and dispersion; probability concepts; the normal distribution;
sampling and sampling distributions; estimation and confidence
intervals; statistical process monitoring and applications in
quality control; use of computers to analyze data; simple linear
regression analysis; multiple regression analysis. Credit for
only one of the following courses may be applied toward
graduation: 101, 104, 105, 227. Credit for both 201 and 227 may
not be applied toward graduation.
Stat 231. Probability and Statistical
Inference for Engineers.
(4-0) Cr. 4. F.S. Prereq: Credit or enrollment in Math 265.
Emphasis on engineering applications. Basic probability; random
variables and probability distributions; joint and sampling
distributions; propagation of error. Descriptive statistics;
confidence intervals; hypothesis testing; simple linear
regression; multiple linear regression; one way analysis of
variance; use of statistical software.
Stat 305. Engineering Statistics.
(3-0) Cr. 3. F.S. Prereq: Math 165 (or 165H). Statistics for
engineering problem solving with emphasis on the design and
analysis of experiments. Descriptive statistics; elementary
probability distributions; principles of experimentation;
confidence intervals and significance tests; one-, two-, and
many-factor studies; regression analysis; use of statistical
software; team project involving multi-factor experimentation and
analysis. Credit for both 105 and 305 may not be applied for
graduation.
Stat 328. Applied Business Statistics.
(2-2) Cr. 3. F.S. Prereq: 201 or 227. Application of statistical
methods to problems in business and economics; review of multiple
regression; residual analysis; model building; analysis of
variance; introduction to experimental design concepts; time
series analysis and forecasting. Nonmajor graduate credit.
Stat 333. Probability and Statistics
for Electrical and Computer Engineers.
(3-0) Cr. 3. F.S. Prereq: Math 267. Accelerated and rigorous
introduction to probability and statistics. Applications to areas
of electrical and computer engineering such as systems, control,
signal processing, digital and analog circuits, communications.
Discrete and continuous random variables, associated probability
models, extensions to random vectors and random processes.
Applications to parameter estimation, confidence intervals,
hypothesis testing, regression, time series, spectral estimation.
Nonmajor graduate credit.
Stat 341. Introduction to the Theory of
Probability and Statistics.
(Same as Math 341.) (3-0) Cr. 3. F.S. Prereq: Math 265 (or 265H).
Probability; distribution functions and their properties;
classical discrete and continuous distributions; moment
generating functions. Credit for both 341 and 447 may not be
applied toward graduation.
Stat 342. Introduction to the Theory of
Probability and Statistics.
(Same as Math 342.) (3-0) Cr. 3. S. Prereq: 341, Math 307 or 317.
Theory of estimation and tests of hypotheses; regression and
correlation; linear model theory; enumerative data.
Stat 361. Quality Control.
(Same as I E 361.) See Industrial Engineering. Nonmajor graduate
credit.
Stat 398. Cooperative Education.
Cr. R. F.S.SS. Prereq: Permission of department head.
Off-campus work periods for undergraduate students in a field of
statistics.
Stat 401. Statistical Methods for Research
Workers.
(3-2) Cr. 4. F.S.SS. Prereq: 101 or 104 or 105 or 201 or 227.
Graduate students without an equivalent course should contact the
department. Methods of analyzing and interpreting experimental
and survey data. Statistical concepts and models; estimation;
hypothesis tests with continuous and discrete data; simple and
multiple linear regression and correlation; introduction to
analysis of variance. Nonmajor graduate credit.
Stat 402. Statistical Design and the
Analysis of Experiments.
(3-0) Cr. 3. F.S. Prereq: 401. The role of statistics in research
and the principles of experimental design. Experimental units,
randomization, replication, blocking, subdividing and repeatedly
measuring experimental units; factorial treatment designs and
confounding; extensions of the analysis of variance to cover
general crossed and nested classifications and models that
include both classificatory and continuous factors. Nonmajor
graduate credit.
Stat 403. Nonparametric Statistical
Methods.
(2-0) Cr. 2. Alt. F., offered 2000. Prereq: 231 or 328 or 401.
Groeneveld. Analysis of data when the dependent variable has
ordinal or nominal properties; statistical inference for ranked
data; Mann-Whitney and Kruskal-Wallis procedures; rank
correlation; efficiency of nonparametric procedures and
robustness of comparable parametric procedures. Nonmajor graduate
credit.
Stat 404. Statistics for the Social
Sciences.
(2-2) Cr. 3. F. Prereq: 401. Lorenz, Roberts. Applications of
generalized linear regression models to social science data.
Assumptions of regression; diagnostics and transformations;
analysis of variance and covariance; path analysis. Nonmajor
graduate credit.
Stat 407. Methods of Multivariate
Analysis.
(2-2) Cr. 3. F. Prereq: 401, knowledge of matrix algebra.
Techniques of analyzing multivariate data including comparing
means using Hotelling’s T2, multivariate analysis of
variance, reducing variable dimension with principal components,
grouping/classifying observations with cluster analysis and
discriminant analysis. Nonmajor graduate credit.
Stat 421. Survey Sampling Techniques.
(2-2) Cr. 3. S. Prereq: 231 or 328 or 401. Methods of designing
and analyzing survey investigations; simple random, stratified,
and multistage sampling designs; methods of estimation including
ratio and regression; construction and use of sample frames.
Nonmajor graduate credit.
Stat 432. Applied Probability Models.
(3-0) Cr. 3. Alt. F., offered 1999. Prereq: 231 or 341 or 447.
Groeneveld. Probabilistic models in engineering and the physical
sciences; probability; Markov chains; Poisson and renewal
processes; applications to queuing, scheduling, control, and
other quantitative problems. Nonmajor graduate credit.
Stat 436. Quantitative Genetics.
(3-0) Cr. 3. S. Prereq: 401. Bailey. Description of the theory of
basic genetic models of quantitative traits. Identification and
discussion of information required for the application of
Quantitative Genetics (QG) theory. Design and analysis of
statistical experiments in QG. Genetic and statistical
implications of natural and artificial selection, including
marker assisted selection, as the basis of genetic improvement.
Nonmajor graduate credit.
Stat 447. Statistical Theory for
Research Workers.
(4-0) Cr. 4. F.S. Prereq: Math 151 and permission of instructor,
or Math 265. Amemiya, Yang. Primarily for graduate students not
majoring in statistics. Emphasis on aspects of the theory
underlying statistical methods. Probability, population
distributions and their properties, sampling distributions, point
and interval estimation, tests of hypotheses, simple regression.
Credit for both 341 and 447 may not be applied toward graduation.
Nonmajor graduate credit
Stat 451. Applied Time Series.
(3-0) Cr. 3. S. Prereq: 231 or 328 or 401. Meeker. Methods for
analyzing data collected over time; review of multiple regression
analysis. Elementary forecasting methods: moving averages and
exponential smoothing. Autoregressive-moving average
(Box-Jenkins) models: identification, estimation, diagnostic
checking, and forecasting. Transfer function models and
intervention analysis. Nonmajor graduate credit.
Stat 479. Computer Processing of
Statistical Data.
(3-0) Cr. 3. F. Prereq: 401. Marasinghe. Structure, content and
programming aspects of a modern statistical package. Advanced
techniques in the use of a statistical software system for data
analysis. Introduction to graphical methods in statistics and a
matrix programming language. Nonmajor graduate credit.
Stat 480. Statistical Applications of
Digital Computers.
(3-0) Cr. 3. S. Prereq: 231 or 328 or 401, Com S 103. Modern
statistical computing. Data management; spread sheets, verifying
data accuracy, transferring data between systems. Data and
graphical analysis with microcomputer statistical software
packages. Macro programming. Simulation. Interface with the World
Wide Wed. Nonmajor graduate credit.
Stat 490. Independent Study.
Cr. var. Prereq: 10 credits in statistics. No more than 9 credits
in Stat 490 may be counted toward graduation. H: Honors.
Stat 493. Workshop in Statistics.
(1-0 or 2-0) Cr. 1 or 2. Off-campus, offered as demand warrants.
Prereq: 101 or 104 or 227. Planning, executing, and interpreting
experiments by understanding experimental design and utilizing
the statistical concepts of linear models. Designed for master of
agriculture program only. Nonmajor graduate credit.
Stat 495. Applied Statistics for
Industry.
(3-0) Cr. 3. F. Prereq: 101 or 104 or 105 or 201 or 227; Math 166
(or 166H). Graduate students without an equivalent course should
consult the department. Statistical thinking applied to
industrial processes. Assessing, monitoring and improving
processes using statistical methods. Analytic/enumerative
studies; graphical displays of data; process monitoring; control
charts; capability analysis. Nonmajor graduate credit.
Stat 496. Applied Statistics for
Industry.
(3-0) Cr. 3. S. Prereq: 495. Statistical design and analysis of
industrial experiments. Concepts of control, randomization and
replication. Simple and multiple regression; factorial and
fractional factorial experiments; reliability; analysis of
lifetime data. Nonmajor graduate credit.
Courses Primarily for Graduate Students, Open to Qualified Undergraduate Students
Stat 500. Statistical Methods.
(3-2) Cr. 4. F. Prereq: 101. Introduction to methods for
analyzing data from experiments and surveys. Graphical data
summaries. Comparison of groups using t-tests, analysis of
variance, and nonparametric analogs. Uses of randomization,
blocking, factorial designs, and nested units in experiments.
Correlation and regression models, model selection and
assessment, effects of collinearity. Introduction to SAS
statistical software.
Stat 501. Multivariate Statistical
Methods.
(3-0) Cr. 3. S. Prereq: 500 or 402; 447 or 542; knowledge of
matrix algebra. Statistical methods for analyzing and displaying
multivariate data: dynamic graphics, principal components, factor
analysis, canonical correlations, cluster analysis,
classification methods, Hotelling’s T2, multivariate
analysis of variance. Statistical software: SAS, S-Plus, and
XGOBI.
Stat 505. Environmental Statistics.
(2-2) Cr. 3. Alt. S., offered 2000. Prereq: 341 or 447; 401.
Basic ideas of statistical modeling for environmental
applications; causation versus association; ecotoxicology; limits
of detection; spatial statistics; geostatistics, kriging, spatial
sampling; hierarchical modeling, Bayesian methodology.
Stat 511. Statistical Methods.
(3-0) Cr. 3. S. Prereq: 500 or 402 or 404; 447 or 542 and current
enrollment in 543; knowledge of matrix algebra. Introduction to
the general theory of linear models, projections and
distributions of quadratic forms; LInear models with both fixed
and random factors, variance components, dealing with missing
data and unbalanced designs. Introduction to non-linear and
generalized linear models, maximum likelihood estimation, local
smoothing methods; Bootstrap and other sample reuse procedures.
Introduction to hierarchical models and Bayesian inference.
Requires use of SAS and S-Plus statistical software.
Stat 512. Design of Experiments.
(3-0) Cr. 3. F. Prereq: 511. Stufken. Basic ideas of experimental
design with applications; completely randomized, randomized
block, and Latin Square designs; randomization analysis;
factorial experiments, confounding, fractional replication;
split-plot and incomplete block designs; crossover designs.
Stat 513. Response Surface Methodology.
(3-0) Cr. 3. Alt. S., offered 2000. Prereq: 402 or 512, knowledge
of elementary matrix theory. Morris. Design criteria and
optimality; determination of optimum operating conditions;
exploration of response surfaces; robust estimation and
transformations; mixture experiments; construction of optimal
designs. Optimization for multiple-response problems.
Stat 514. Scheduling and Inventory
Theory.
(Same as I E 514.) See Industrial Engineering.
Stat 515. Theory and Applications of
Nonlinear Models.
(3-0) Cr. 3. F. Prereq: 447 or 543, 511. Kaiser. Construction of
nonlinear statistical models; random and systematic model
components, review of likelihood-based inferences. Iterative
algorithms for maximum likelihood estimation. Nonlinear
regression models using additive error with nonconstant variance,
transform both sides models, generalized linear models and their
extensions. Introduction to compartment models, growth curves and
pharmaco-kinetic models. Basic random parameter models,
beta-binomial and gamma-Poisson mixtures. Requires use of
instructor-supplied and student-written S-plus functions.
Stat 521. Theory and Applications of
Sample Surveys.
(3-0) Cr. 3. S. Prereq: 401; 447 or 542. Breidt, Opsomer.
Practical aspects and basic theory of design and estimation in
sample surveys for finite populations, with emphasis on
applications. Simple random, systematic, stratified, cluster and
multistage sampling. General unequal probability designs.
Horvitz-Thompson estimation of totals and functions of totals:
means, proportions, covariances, regression coefficients.
Model-assisted ratio and regression estimation. Two-phase
sampling. Variance estimation for complex designs. Nonsampling
errors.
Stat 531. Quality Control and
Engineering Statistics.
(Same as I E 531.) (3-0) Cr. 3., Alt. S., offered 2001. Prereq:
401; 342 or 447. Vardeman. Statistical methods and theory
applicable to problems of industrial process monitoring and
improvement. Statistical issues in industrial measurement;
Shewhart, CUSUM, and other control charts; feedback control;
process capability studies; estimation of product and process
characteristics; acceptance sampling, continuous sampling and
sequential sampling; economic and decision theoretic arguments in
industrial statistics; experimentation for process improvement.
Stat 533. Reliability.
(Same as I E 533.) (3-0) Cr. 3. Alt. S., offered 2000. Prereq:
342 or 432 or 447. Meeker. Probabilistic modeling and inference
in reliability; analysis of systems; Bayesian aspects; product
limit estimator, probability plotting, maximum likelihood
estimation for censored data, accelerated failure time and
proportional hazards regression models with applications to
accelerated life testing; repairable system data; planning
studies to obtain reliability data.
Stat 534. Ecological Statistics.
(3-0) Cr. 3. Alt. S., offered 2001. Prereq: 447 or 542.
Statistical methods for analysis of data from ecological field
studies. Sampling strategies for estimation of diversity and
species richness. Comparison of ecological quantities among
regions and across time. Statistical formulation of ecological
concepts such as competition and biodiversity. Effects of time
and space on population dynamics models. Ordination and analysis
of complex multivariate data. Statistical methods discussed will
include randomization and permutation tests, spatial point
processes, bootstrap estimation of standard error, changepoint
regression models, random parameter models and Empirical Bayes
methods.
Stat 535. Methods in Biostatistics.
(3-0) Cr. 3. Alt. F., offered 1999. Prereq: 500; 543 or 447.
Daniels. Statistical methods useful for biostatistical problems.
Topics include analysis of observational studies and randomized
clinical trials, techniques in the analysis of survival and
longitudinal data, approaches to handling missing data, and
meta-analysis. Examples will come from recent studies in cancer,
AIDS, heart disease and psychiatry and from studies to evaluate
health care in the U.S. (health services research).
Stat 536. Genetic Statistics.
(Same as Gen 536.) (3-0) Cr. 3. Alt. F., offered 1999. Prereq:
401, 447; Gen 320 or Biol 301 or permission of instructor.
Pollak. Probability applied to genetic systems; random mating;
selection, mutation and migration; theory of inbreeding; effects
of finite population size; basic concepts in quantitative
genetics; prediction of progress from artificial selection.
Stat 537. Genetic Statistics.
(Same as Gen 537.) (3-0) Cr. 3. Alt. F., offered 2000. Prereq:
536 or permission of instructor. Sampling designs and
experimental designs to obtain information from markers;
detecting major genes; linkage analysis and segregation analysis;
finding alignments and similarities between DNA sequences;
constructing phylogenetic trees.
Stat 538. Econometric Statistics.
(Same as Econ 538.) (3-0) Cr. 3. F. Prereq: 542 or Econ 573.
Bilias. Generalized linear regression, nonlinear regression,
measurement error models. Simultaneous equation systems,
regression equations with autoregressive errors, large sample
theory.
Stat 539. Game Theory.
(Same as Econ 539, I E 539.) (3-0) Cr. 3. F. Prereq: 341 or 432
or 447. Zero-sum and bi-matrix non-cooperative two person games;
games of timing; relation to mathematical programming;
cooperative n-person games.
Stat 542. Theory of Probability and
Statistics.
(4-0) Cr. 4. F. Prereq: 341; Math 414 or 465. Sample spaces,
probability, conditional probability; Random variables,
expectation, inequalities; Common theoretical distributions;
Joint distributions, conditional distributions, introduction to
Bayesian inference; Introduction to point estimation including
maximum likelihood estimation, method of moments, basic
properties of point estimators; Stochastic processes with
applications to Poisson Process, Brownian motion; Moment
generating functions and characteristic functions; Probability
laws of transformations, sampling distributions, order
statistics.
Stat 543. Theory of Probability and
Statistics.
(3-0) Cr. 3. S. Prereq: 542. Point estimation including maximum
likelihood estimation, Bayes estimators, Bayesian and minimax
optimality, unbiasedness, sufficiency, completeness, Basu’s
theorem; Convergence in probability, convergence in distribution,
laws of large numbers, central limit theorem; Confidence
intervals, prediction intervals; Hypothesis testing,
Neyman-Pearson Lemma, uniformly most powerful tests, likelihood
ratio tests; Bayesian interval estimation and tests;
Nonparametric methods, bootstrap.
Stat 544. Bayesian Statistics.
(3-0) Cr. 3. S. Prereq: 543. Stern. Specification of probability
models; subjective, conjugate, and noninformative distributions;
hierarchical models; analytical and computational techniques for
obtaining posterior distributions; model checking, model
selection, diagnostics; comparison of Bayesian and traditional
methods; empirical Bayes procedures; decision theory.
Stat 546. Theory of Nonparametric and
Asymptotic Methods.
(3-0) Cr. 3. Alt. S., offered 2001. Prereq: 542. Sukhatme.
Introduction to nonparametric problems; tests based upon sample
distribution functions, rank tests for location, scale and
independence; local properties of rank tests; convergence of a
sequence of random variables; limit theorems; asymptotic
distributions of sample quantiles, U-statistics, rank statistics,
chi-square and other goodness of fit test statistics; asymptotic
efficiency of tests.
Stat 551. Time Series Analysis.
(3-0) Cr. 3. F. Prereq: 447 or 542. Stationary and non-stationary
time series; covariance and spectral properties of stationary
time series; autoregressive moving average processes; best linear
prediction; state space models and Kalman recursions; estimation
techniques, model-building and diagnostics.
Stat 554. Introduction to Stochastic
Processes.
(Same as Math 554.) See Mathematics.
Stat 555. Theory of Stochastic
Processes.
(Same as Math 555.) See Mathematics.
Stat 557. Statistical Methods for
Counts and Proportions.
(3-0) Cr. 3. Alt. F., offered 2000. Prereq: 500 or 401; 543 or
447. Koehler. Statistical methods for analyzing simple random
samples when outcomes are counts or proportions; measures of
association and relative risk, chi-squared tests, loglinear
models, logistic regression and other generalized linear models,
extensions to longitudinal studies and nested designs. Maximum
likelihood estimation, generalized estimating equations. Use of
statistical software: SAS and S-Plus.
Stat 579. Orientation to Software
Systems for Statistical Computing.
(1-0) Cr. 1. F. Prereq: Graduate classification in statistics.
Kennedy, Marasinghe. Orientation to scientific and statistical
software available on campus. Offered on a satisfactory-fail
grading basis only.
Stat 580. Computational Methods in
Statistics.
(3-0) Cr. 3. S. Prereq: 500, 542. Marasinghe. Linear and
nonlinear least computations, computations associated with
maximum likelihood squares and regression estimation problems,
Monte Carlo methods in statistics research, computer intensive
applications of applications including the bootstrap, evaluation
of multiple integrals, EMalgorithm, etc. Assignments will include
applications of these methods using the S-Plus programming
language.
Stat 581. Advanced Statistical
Computing.
(3-0) Cr. 3. Alt. F., offered 1999. Prereq: 511, 580 and
programming in a scientific language. Marasinghe, Kennedy.
Numerical computations and algorithms with applications in
statistics.These include discussions on random number generation,
solution of nonlinear equations, optimization methods, matrix
linear algebra and numerical integration.
Stat 590. Special Topics.
Cr. var.
A. Theory
B. Methods
C. Design of Experiments
D. Design of Surveys
Stat 599. Creative Component.
Courses for Graduate Students
Stat 601. Advanced Statistical Methods.
(3-0) Cr. 3. Alt. S., offered 2001. Prereq: 511; Math 514.
Kaiser. This course is designed to provide students with in-depth
coverage of topics from current and recent developments in
statistical modeling and applications. Recent topics have
included Markov Chain Monte Carlo methods for Bayesian analysis
of hierarchical models, conditionally specified statistical
models, complex random parameter models, and Bayesian dynamic
models. Applications have included problems of monitoring air and
water quality, spatial modeling of organism abundance and disease
rates, and population pharmacokinetic models. Requires some
programming ability to deal with computationally intensive
methods.
Stat 606. Spatial Statistics.
(3-0) Cr. 3. Alt. S., offered 2001. Prereq: 511, 543. General
spatial models; spatial data analysis; continuous spatial
variation, geostatistics, kriging; lattice data, conditional
models, joint models; image analysis; point patterns, randomness,
clustering, random sets.
Stat 611. Theory and Applications of
Linear Models.
(3-0) Cr. 3. F. Prereq: 500 or 402 or 404, 542 or 447, a course
in matrix algebra. Stufken, Wu. Matrix preliminaries,
estimability, theory of least squares and of best linear unbiased
estimation, analysis of variance and covariance, distribution of
quadratic forms, extension of theory to mixed and random models,
inference for variance components.
Stat 612. Advanced Design of
Experiments.
(3-0) Cr. 3. Alt. S., offered 2001 Prereq: 512. Stufken. Design
optimality criteria and optimal designs; Galois fields and finite
geometries with applications to design construction; fractional
factorial designs; theory of approximate designs and the
equivalence theorem; crossover designs with applications.
Stat 621. Advanced Theory of Survey
Sampling.
(3-0) Cr. 3. Alt. S., offered 2000. Prereq: 521. Breidt. Advanced
topics of current interest in design of surveys and analysis of
survey data; criteria for choice of survey strategies including
sufficiency, likelihood, and admissibility; super population
models and their role in choice of optimal strategies; review of
recent literature.
Stat 642. Advanced Probability Theory.
(3-0) Cr. 3. S. Prereq: 542, Math 514. Athreya, Lahiri.
Probability spaces; Kolmogorov’s existence theorem for
stochastic processes; expectation; Jensen’s inequality and
applications; Borel-Cantelli lemmas; Weak and strong laws of
large numbers; convergence of moments; weak convergence of
probability distributions; characteristic functions; continuity
theorem; Lindeberg-Feller central limit theorem and its
ramifications; conditional expectation and probability; discrete
time martingales, renewal theory and Markov chains, Brownian
motion.
Stat 643. Theory of Estimation and
Testing of Hypotheses.
(3-0) Cr. 3. F. Prereq: 543, 642. Lahiri, Vardeman. Sufficiency
completeness; Elements of decision theory; Bayesian paradigm of
inference and theory of Markov Chain Monte Carlo; Invariance;
Neyman-Pearson theory of testing hypotheses. Uniformly most
powerful tests, introduction to unbiased tests, likelihood ratio
tests,.Wald’s tests, Rao’s tests; Asymptotic theory of
maximum likelihood estimation and likelihood ratio tests;
Asymptotic efficiency; Resampling methods.
Stat 645. Order Statistics.
(3-0) Cr. 3. Alt. F., offered 1999. Prereq: 543. Distribution
theory and moments of order statistics; estimation of location
and scale parameters; censoring; robust estimation; treatment of
outliers; asymptotic distributions of quantiles, extremes, and
linear functions of order statistics.
Stat 647. Multivariate Analysis.
(3-0) Cr. 3. F. Prereq: 543, knowledge of matrix algebra.
Amemiya. Multivariate normal distribution, Wishart distribution,
multiple, partial, and canonical correlations, inference for mean
vector, multivariate regression, principal components,
discriminant analysis, factor analysis, covariance structure
analysis, latent variable modeling.
Stat 648. Seminar on Theory of
Statistics and Probability.
Cr. var. Alt. S., offered 2000. Prereq: 643.
Stat 651. Time Series.
(3-0) Cr. 3. Alt. S., offered 2000. Prereq: 551, 642. Fuller.
Covariance and spectral representation of time series. Stationary
and nonstationary autoregressive models. Fourier and periodogram
analyses. Stochastic difference equations. Estimation and
distribution theory.
Stat 680. Advanced Statistical
Computing.
(3-0) Cr. 3. Alt. S., offered 2000. Prereq: 580. Cook, Kennedy.
Selected methods and algorithms in selected areas of statistical
computing. Emphasis on the most recent advances in these and
other areas supported by statistical computing.
Stat 690. Advanced Special Topics.
Cr. Var. Prereq: Permission of instructor.
A. Theory
B. Methods
C. Design of Experiments
D. Design of Surveys
E. Statistical Computing
F. Graphics
Stat 699. Research.