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Course Criteria
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3.00 Credits
Course Level: Graduate Theory of estimation, properties of estimators, large-sample properties and techniques, and applications. Usually offered every fall. Prerequisite: STAT-531 and MATH-574 (may be taken concurrently).
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3.00 Credits
Course Level: Graduate Topics vary by section, may be repeated for credit with different topic. Mathematical foundations of statistical theory. Special topics in probability and mathematical statistics. Usually offered alternate springs (odd years). Prerequisite: permission of instructor.
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3.00 Credits
Course Level: Graduate The mathematical foundations of statistical inference; the Theory of Estimation including minimum risk-, Bayes-, minimax-, and equivariant estimation; decision theory; and large sample behavior. Usually offered alternate falls (even years). Prerequisite: STAT-600.
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3.00 Credits
Course Level: Graduate Extension of regression methodology to more general settings where standard assumptions for ordinary least squares are violated. Generalized least squares, robust regression, bootstrap, regression in the presence of auto-correlated errors, generalized linear models, logistic and Poisson regression. Usually offered every spring. Prerequisite: STAT-515.
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3.00 Credits
Course Level: Graduate Multivariate normal distribution, Hotelling's T2, Wilks's likelihood ratio criterion, other test statistics, classification problems, principal components, canonical correlation, general multivariate regression and experimental designs, and related subjects. Usually offered alternate falls (even years). Prerequisite: STAT-600 (may be taken concurrently).
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3.00 Credits
Course Level: Graduate Multivariate normal distribution, Hotelling's T2, Wilks's likelihood ratio criterion, other test statistics, classification problems, principal components, canonical correlation, general multivariate regression and experimental designs, and related subjects. Usually offered alternate springs (even years). Prerequisite: STAT-600 (may be taken concurrently).
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3.00 Credits
Course Level: Graduate An introduction to numerical analysis, computer science, and statistical theory as they apply to random number generation, the Monte Carlo method, simulations, and other aspects of statistical computing. Usually offered every spring. Prerequisite: permission of instructor.
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3.00 Credits
Course Level: Graduate General linear hypothesis, least-squares estimation, Gauss-Markov theorem, regression, analysis of variance, multiple comparisons, analysis of covariance, factorial designs, randomized blocks, other experimental designs, and effects of departures from assumptions. Usually offered alternate falls (odd years). Prerequisite: STAT-600 (may be taken concurrently).
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3.00 Credits
Course Level: Graduate General linear hypothesis, least-squares estimation, Gauss-Markov theorem, regression, analysis of variance, multiple comparisons, analysis of covariance, factorial designs, randomized blocks, other experimental designs, and effects ofdepartures from assumptions. Usually offered alternate springs (even years). Prerequisite: STAT-600 (may be taken concurrently).
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1.00 - 6.00 Credits
Course Level: Graduate Prerequisite: permission of instructor and department chair.
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