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, 226.
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; elementary probability; binomial and normal distributions; regression; estimation and hypothesis testing. For students in the agricultural and biological sciences. Credit for only one of the following courses may be applied toward graduation: 101, 104, 105, 226.
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, 226. Credit for both 105 and 305 may not be applied for graduation.
Stat 226. Introduction to Business Statistics I. (3-0) Cr. 3. F.S.SS. Prereq: Math 150 or 165. Obtaining, presenting, and organizing statistical data; measures of location and dispersion; the Normal distribution; sampling and sampling distributions; estimation and confidence intervals; interference for simple linear regression analysis; use of computers to visualize and analyze data. Credit for only one of the following courses may be applied toward graduation: 101, 104, 105, 226.
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. 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.SS. Prereq: Math 165 (or 165H). Statistics for engineering problem solving. Principles of engineering data collection; descriptive statistics; elementary probability distributions; principles of experimentation; confidence intervals and significance tests; one-, two-, and multi-sample studies; regression analysis; use of statistical software; team project involving engineering experimentation and data analysis. Credit for both 105 and 305 may not be applied for graduation.
Stat 322. Probabilistic Methods for Electrical Engineers. (Same as E E 322.) (3-0) Cr. 3. F.S. Prereq: E E 224. Introduction to probability with applications to electrical engineering. Sets and events, probability space, conditional probability, total probability and Bayes' rule. Discrete and continuous random variables, cumulative distribution function, probability mass and density functions, expectation, moments, moment generating functions, multiple random variables, functions of random variables. Elements of statistics, hypothesis testing, confidence intervals, least squares. Introduction to random processes.
Stat 326. Introduction to Business Statistics II. (2-2) Cr. 3. F.S. Prereq: 226. Multiple regression analysis; regression diagnostics; model building; applications in analysis of variance and time series; random variables; distributions; conditional probability; statistical process control methods; use of computers to visualize and analyze data.
Stat 328. Applied Business Statistics. (2-2) Cr. 3. F.S. Prereq: 326, primarily for MBA students. 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 330. Probability and Statistics for Computer Science. (3-0) Cr. 3. F.S. Prereq: Math 166. Topics from probability and statistics applicable to computer science. Basic probability; Random variables and their distributions; Elementary probabilistic simulation; Queuing models; Basic statistical inference; Introduction to regression.Nonmajor graduate credit.
Stat 332.(X) Visual Communication of Quantitative Information (Same as English 332X.) Cr. 3. Prereq: Statistics 101, 104, or 226; English 104; and English 105. Communicating quantitative information using visual displays: visualizing data, interactive and dynamic data displays, evaluating current examples in the media, color/perception/representation in graphs, interpreting data displays.
Stat 341. Introduction to the Theory of Probability and Statistics I (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 distribution functions; moment generating functions, multivariate probability distributions and their properties. Credit for both 341 and 447 may not be applied toward graduation.
Stat 342. Introduction to the Theory of Probability and Statistics II (Same as Math 342). (3-0) Cr. 3. S. Prereq: 341, Math 307 or 317. Transformations of random variables; sampling distributions; confidence intervals; theory of estimation and tests of hypotheses; linear model theory.
Stat 401. Statistical Methods for Research Workers. (3-2) Cr. 4. F.S.SS. Prereq: 101 or 104 or 105 or 226. 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 and blocking. 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. Determining sample size. Nonmajor graduate credit.
Stat 404. Regression for Social and Behavioral Research. (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 406. Statistical Methods for Spatial Data (Dual-listed with 506). (3-0) Cr. 3. Alt. S., offered 2006. Prereq: Six hours of statistics at the 400-level. The analysis of spatial data; geostatistical methods and spatial prediction; discrete index random fields and Markov random field models; models for spatial point processes. Emphasis on application and practical use of spatial statistical analysis. Nonmajor graduate credit.
Stat 407. Methods of Multivariate Analysis. (2-2) Cr. 3. F. Prereq: 401, knowledge of matrix algebra. Carriquiry, Cook. Techniques for analyzing multivariate data including comparing group mean vectors using Hotelling's T2, multivariate analysis of variance, reducing variable dimension with principal components, grouping/classifying observations with cluster analysis and discriminant analysis. Imputation of missing multivariate observations. Nonmajor graduate credit.
Stat 415. Advanced Statistical Methods for Research Workers. (2-2) Cr. 3. Alt. S., offered 2007. Prereq: 401. Advanced statistical methods using modern computer methods for modeling and analyzing data. Examples from a wide variety of scientific and engineering disciplines. Nonmajor graduate credit.
Stat 416.(X) Statistical Design and Analysis of Microarray Experiments. Cr. 3. Prereq: Stat 401. Nettleton. Introduction to two-color microarray technology including cDNA and oligo microarrays; introduction to single-channel platforms (nylon membrane arrays, Affymetrix GeneChips); the role of blocking, randomization, and biological and technical replication in microarray experiments; design of single-channel and two-color microarray experiments with factorial treatment structure; normalization methods; mixed linear model analyses to identify differentially expressed genes; adjustments for multiple testing; alternative analysis strategies including clustering and hierarchical modeling of microarray data; emphasis on practical use of methods. Nonmajor graduate credit.
Stat 421. Survey Sampling Techniques. (2-2) Cr. 3. S. Prereq: 231 or 328 or 401. Concepts of sample surveys and the survey process; methods of designing sample surveys, including: simple random, stratified, and multistage sampling designs; methods of analyzing sample surveys including ratio, regression, domain estimation and nonresponse. Nonmajor graduate credit.
Stat 430.(X) Empirical Methods for Computer Science. Cr. 3. Prereq: Stat 330 or an equivalent course. Ghosh. Programs and systems as objects of empirical studies; exploratory data analysis; analysis of designed experiments – analysis of variance, hypothesis testing, interaction among variables; linear regression, logistic regression, Poisson regression; parameter estimation, prediction, confidence regions, dimension reduction techniques, model diagnostics and sensitivity analysis; simulation techniques and bootstrap methods; applications to performance assessment – comparison of multiple systems; communicating results of empirical studies. Nonmajor graduate credit.
Stat 432. Applied Probability Models. (3-0) Cr. 3. F. Prereq: 231 or 341 or 447. Probabilistic models in biological, engineering and the physical sciences. Markov chains; Poisson, birth-and-death, renewal, branching and queing processes; applications to bioinformatics and other quantitative problems. Nonmajor graduate credit.
Stat 447. Statistical Theory for Research Workers. (4-0) Cr. 4. F.S.SS. Prereq: Math 151 and permission of instructor, or Math 265. 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 macro programming language. Currently SAS is the software system used. Nonmajor graduate credit.
Stat 480. Statistical Computing Applications. (3-0) Cr. 3. S. Prereq: 231 or 328 or 401. 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. Algorithmic programming concepts and applications. Simulation. Software reliability. Nonmajor graduate credit.
Stat 493. Workshop in Statistics. (2-0) Cr. 2. Off-campus, offered as demand warrants. Prereq: 101 or 104 or 226. Introduction to methods for analyzing data from surveys and experiments. Summarizing data, analysis of data from simple random samples and more complex survey designs, experimental design, estimation and hypothesis testing for data from simple experiments. Designed for master of agriculture program only. Nonmajor graduate credit.
Stat 495. Applied Statistics for Industry I. (3-0) Cr. 3. Alt. F., offered 2006. Prereq: 101 or 104 or 105 or 226; 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 II. (3-0) Cr. 3. Alt. S., offered 2007. 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.
Stat 500. Statistical Methods. (3-2) Cr. 4. F. Prereq: 101. Introduction to methods for analyzing data from experiments and observational data. Design-based and model-based inference. Estimation, hypothesis testing, and model assessment for 2 group and k group studies. Experimental design and the use of pairing/blocking. Analysis of discrete data. Correlation and regression, prediction, model selection and diagnostics. Simple mixed models including nested random effects and split plot experimental designs. Use of the 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: simultaneous analysis of multiple responses, multivariate analysis of variance; summarizing high dimensional data with principal components, factor analysis, canonical correlations, multidimensional scaling; grouping similar items with cluster analysis; classification methods; dynamic graphics. Statistical software: SAS, S-Plus or R, and GGobi.
Stat 503. Exploratory Methods and Data Mining. (2-2) Cr. 3. Alt. S., offered 2007. Prereq: 401, 341 or 447. Approaches to finding the unexpected in data; pattern recognition, classification, association rules, graphical methods, classical and computer- intensive statistical techniques, and problem solving. Emphasis is on data-centered, non-inferential statistics for large or high-dimensional data, topical problems, and building report writing skills.
Stat 505. Environmental Statistics. (2-2) Cr. 3. Alt. S., offered 2006. 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 506. Statistical Methods for Spatial Data (Dual listed with 406).(3-0) Cr. 3. Alt. S., offered 2006. Prereq: 447 or 542. The analysis of spatial data; geostatistical methods and spatial prediction; discrete index random fields and Markkov random field models; models for spatial point processes.
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, least squares and maximum likelihood estimation, hypothesis testing, interval estimation and prediction, analysis of unbalanced designs. Models with both fixed and random factors. Introduction to non-linear and generalized linear models, bootstrap estimation, local smoothing methods. Requires use of R statistical software.
Stat 512. Design of Experiments. (3-0) Cr. 3. F. Prereq: 511. Basic ideas of experimental design and analysis; completely randomized, randomized complete 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 2006. Prereq: 402 or 512, knowledge of elementary matrix theory and matrix formulation of regression. Morris. Analysis techniques for locating optimum and near-optimum operating conditions: standard experimental designs for first- and second-order response surface models; design performance criteria; use of data transformations; mixture experiments; optimization for multiple-response problems. Requires use of statistical software with matrix functions.
Stat 515. Theory and Applications of Nonlinear Models. (3-0) Cr. 3. F. Prereq: 447 or 543, 511. 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 516.(X) Statistical Design and Analysis of Microarray Experiments. Cr. 3. Prereq: Stat 500. Nettleton. Introduction to two-color microarray technology including cDNA and oligo microarrays; introduction to single-channel platforms (nylon membrane arrays, Affymetrix GeneChips); the role of blocking, randomization, and biological and technical replication in microarray experiments; design of single-channel and two-color microarray experiments with factorial treatment structure; normalization methods; mixed linear model analyses to identify differentially expressed genes; adjustments for multiple testing; alternative analysis strategies including clustering and hierarchical modeling of microarray data; current research topics statistics for microarrays.
Stat 521. Theory and Applications of Sample Surveys. (3-0) Cr. 3. S. Prereq: 401; 447 or 542. Practical aspects and basic theory of design and estimation in sample surveys for finite populations. Simple random, systematic, stratified, cluster multistage and unequal-probability sampling. Horvitz- Thompson estimation of totals and functions of totals: means, proportions, regression coefficients. Linearization technique for variance estimation. Model-assisted ratio and regression estimation. Two- phase sampling and sampling on two occasions. Non-response effects. Imputation.
Stat 522X. Advanced Applied Survey Sampling. (3-0) Cr. 3. Alt. F., offered 2007. Prereq: Stat 521 or both Stat 421 and Stat 477. Advanced topics in survey sampling and methodology: clustering and stratification in practice, adjustments and imputation for missing data, variance estimation in complex surveys, methods of panel and/or longitudinal surveys, procedures to increase response rates, and computing. Examples are taken from large, well-known surveys in various subject areas. Prior exposure to mathematical statistics, probability, and at least one course in survey sampling theory is assumed.
Stat 528. Applied Business Statistics. (2-2) Cr. 3. F.SS. Prereq: 226 and enrollment in MBA, not for STAT majors. 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 531. Quality Control and Engineering Statistic (Same as I E 531).(3-0) Cr. 3., Alt. S., offered 2007. Prereq: 401; 342 or 447. Wu. 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 characterization studies; estimation of product and process characteristics; acceptance sampling, continuous sampling and sequential sampling; economic and decision theoretic arguments in industrial statistics.
Stat 533. Reliability (Same as I E 533). (3-0) Cr. 3. Alt. S., offered 2006. 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. F., offered 2005. Prereq: 447 or 542. Dixon. Statistical methods for non-standard problems, illustrated using questions and data from ecological field studies. Specific topics include: Estimation of abundance and survival from mark-recapture studies. Deterministic and stochastic matrix models of population trends. Estimation of species richness and diversity. Ordination and analysis of complex multivariate data. Statistical methods discussed will include randomization and permutation tests, spatial point processes, bootstrap estimation of standard error, partial likelihood and Empirical Bayes methods.
Stat 536. Statistics for Population Genetics (Same as GDCB 536). (3-0) Cr. 3. Alt. F., offered 2006. Prereq: 401, 447; Gen 320 or Biol 313. Statistical models for population genetics covering; selection, mutation, migration, population structure, and linkage disequilibrium. Applications to gene mapping (case-control, TDT), inference about population structure, DNA and protein sequence analysis, and forensic and paternity identification.
Stat 537. Statistics for Molecular Genetics. (Same as GDCB 537.) (3-0) Cr. 3. Alt. S., offered 2007. Statistical models, inference, and computational tools for linkage analysis, quantitative trait analysis, and molecular evolution. Topics include; quantitative trait models, variance component mapping, interval and composite-interval mapping, and phylogenetic tree reconstruction.
Stat 542. Theory of Probability and Statistics I. (4-0) Cr. 4. F. Prereq: 341; Math 414 or 465. Sample spaces, probability, conditional probability; Random variables, univariate distributions, expectation, moment generating functions; Common theoretical distributions; Joint distributions, conditional distributions and independence, covariance; Probability laws and transformations; Introduction to the Multivariate Normal distribution; Sampling distributions, order statistics; Convergence concepts, the central limit theorem and delta method; Basics of stochastic simulation.
Stat 543. Theory of Probability and Statistics II. (3-0) Cr. 3. S. Prereq: 542. Point estimation including method of moments, maximum likelihood estimation, exponential family, Bayes estimators, Loss function and Bayesian optimality, unbiasedness, sufficiency, completeness, Basu's theorem; Interval estimation including conficence intervals, prediction intervals, Bayesian interval estimation; Hypothesis testing including Neyman-Pearson Lemma, uniformly most powerful tests, likelihood ratio tests; Bayesian tests; Nonparametric methods, bootstrap.
Stat 544. Bayesian Statistics. (3-0) Cr. 3. S. Prereq: 543. Specification of probability models; subjective, conjugate, and noninformative prior distributions; hierarchical models; analytical and computational techniques for obtaining posterior distributions; model checking, model selection, diagnostics; comparison of Bayesian and traditional methods.
Stat 546. Nonparametric Methods in Statistics. (3-0) Cr. 3. Alt. S., offered 2007. Prereq: 511, 542. Chen, Opsomer. Overview of parametric versus nonparametric methods of inference; introduction to nonparametric smoothing methods for estimating density and regression functions; smoothing parameter selection; applications to semiparametric models and goodness-of-fit tests of a parametric model.
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; estimation techniques, model-building and diagnostics.
Stat 552.(X) Advanced Applied Survey Sampling. Cr. 3. Prereq: Stat 521 or both Stat 421 and Stat 477. Larsen. Advanced topics in survey sampling and methodology: clustering and stratification in practice, adjustments and imputation for missing data, variance estimation in complex surveys, methods of panel and/or longitudinal surveys, procedures to increase response rates, and computing. Examples are taken from large, well-known surveys in various subject areas. Prior exposure to mathematical statistics, probability, and at least one course in survey sampling theory is assumed.
Stat 557. Statistical Methods for Counts and Proportions. (3-0) Cr. 3. Alt. F., offered 2006. Prereq: 500 or 401; 543 or 447. 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 complex designs, models with fixed and random effects. Use of statistical software: SAS, S-Plus or R.
Stat 565. Methods in Biostatistics (Same as Tox 565).(3-0) Cr. 3. Alt. F., offered 2005. Prereq: 500; 543 or 447. Statistical methods useful for biostatistical problems. Topics include analysis of cohort studies, case-control studies and randomized clinical trials, techniques in the analysis of survival data and longitudinal studies, approaches to handling missing data, and meta-analysis. Examples will come from recent studies in cancer, AIDS, heart disease, psychiatry and other human and animal health studies
Stat 579. Introduction to Statistical Computing. (0-2) Cr. 1. F. Prereq: Enrollment in 500. An introduction to the logic of programming, numerical algorithms, and graphics. The statistical package R will be used to demonstrate how data can be stored, manipulated, plotted, and analyzed using both built-in functions and user extensions. Concepts of modularization, looping, vectorization, conditional execution, and recursion will be emphasized.
Stat 580. Statistical Computing - I. (3-0) Cr. 3. S. Prereq: 579 and 447 or 542. Introduction to scientific computing for statistics using tools and concepts in R: programming tools, modern programming methodologies, modularization, design of statistical algorithms. Introduction to C programming for efficiency; interfacing R with C. Building statistical libraries. Use of algorithms in modern subroutine packages, optimization and integration. Implementation of simulation methods; inversion of probability integral transform, rejection sampling, importance sampling. Monte Carlo integration.
Stat 581. Statistical Computing - II. (3-0) Cr. 3. Alt. F., offered 2005. Prereq: 543 and 580. Normal approximations to likelihoods. The delta-method and propagation of errors. Topics in the use of the E- M algorithm including; its use in the exponential family, computation of standard errors, acceleration. Resampling methods: brief theory and application of the jackknife and the bootstrap. Randomization tests. Stochastic simulation: Markov Chain, Monte Carlo, Gibbs' sampling, Hastings-Metropolis algorithms, critical slowing-down and remedies, auxiliary variables, simulated tempering, reversible- jump MCMC and multi-grid methods.
Stat 601. Advanced Statistical Methods. (3-2) Cr. 4. F. Prereq: 511, 543. Approaches and fundamental methods connected with those approaches statisticians take toward the analysis of scientific problems. Students develop an understanding of the way that various concepts of probability are used in problem formulation, analysis, and inference, and the ability to develop one or more appropriate analyses for a variety of problems. Specific methodological topics include permutation procedures and design-based analysis; model building with single and multiple stochastic components; estimation based on least-squares, likelihood, approximate likelihood, sample reuse, and simulation; inference in the sample space, parameter space, and belief space. Development of various analyses for real problems, including statistical formulation and necessary computations.
Stat 606. Advanced Spatial Statistics. (3-0) Cr. 3. Alt. S., offered 2007. Prereq: 506, 642. General formulation of spatial models; construction of nonstationary covariance functions; conditional and simultaneous model specification; random measures and point processes; spatio-temporal models. Estimation and distribution theory.
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. 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 2007. Prereq: 512. Design optimality criteria, approximate design and general equivalence theory, computational approaches to constructing optimal designs for linear models. Advanced topics of current interest in the design of experiments, including one or more of: distance based design criteria and construction of spatial process models, screening design strategies for high-dimensional problems, and design problems associated with computational experiments.
Stat 615.(X) Nonlinear Mixed Models: Theory, Methods and Applications. Cr. 3. Prereq: Stat 601 and Stat 611. Maiti. The linear mixed effects (LME) model, the generalized linear mixed effects model (GLMM), quasilikelihood estimation, generalized estimating equations, nonlinear mixed effects (NLME) model, application in longitudinal data analysis, growth curve analysis and small area estimation, method of model diagnostics and influential analysis. The knowledge of general statistical inference is assumed.
Stat 621. Advanced Theory of Survey Sampling. (3-0) Cr. 3. Alt. F., offered 2006. Prereq: 521. Advanced topics of current interest in the design of surveys and analysis of survey data, including: asymptotic theory for design and model-based estimators, use of auxiliary information in estimation, variance estimation techniques, small area estimation, non-response modeling and imputation.
Stat 642. Advanced Probability Theory. (Same as Math 642.) (4-0) Cr. 4. F. Prereq: 542. Measure spaces, extension of measures, Lebesque integration and convergence theorem, Lp-spaces, absolute continuity, Radon-Nikodym Theorem, product spaces and Fubini-Tonelli Theorems; 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.
Stat 643. Advanced Theory of Statistical Inference. (4-0) Cr. 4. S. Prereq: 543, 642. Weak convergence of probability distributions; characteristic functions; continuity theorem; Lindberg-Feller central limit theorem and its ramifications; conditional expectation and probability; sufficiency, completeness; Elements of decision theory; Neyman-Pearson theory of testing hypotheses. Uniformly most powerful tests, introduction to unbiased tests, likelihood ratio tests,.Asymptotic theory of maximum likelihood estimation and likelihood ratio tests; Invariance.
Stat 647. Multivariate Analysis. (3-0) Cr. 3. Alt. S., offered 2006. Prereq: 543, knowledge of matrix algebra. Multivariate normal distribution, estimation of the mean vector and the covariance matrix, multiple and partial correlation, Hotelling's T2 statistic, Wishart distribution, multivariate regression, principle components, discriminant analysis, factor analysis, high dimensional data analysis.
Stat 651. Time Series. (3-0) Cr. 3. Alt. S., offered 2006. Prereq: 551, 642. Covariance and spectral representation of time series. Stationary and nonstationary autoregressive models. Fourier and periodogram analyses. Stochastic difference equations. Estimation and distribution theory, long range dependence.