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Course Criteria
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3.00 Credits
Credit Hours: 3.00. Computationally intensive methods in statistics including bootstrapping, Monte Carlo simulation, nonparametric density estimation, nonparametric regression, and methods appropriate for high-dimensional data. Extensive use is made of statistical software. Typically offered Fall Spring. 3.000 Credit Hours Levels: Graduate, Professional, Undergraduate Schedule Types: Lecture College of Science College Statistics Department Course Attributes: CH Technical Electives, Upper Division
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3.00 Credits
Credit Hours: 3.00. Distribution of mean and s2 in normal samples, sampling distributions derived from the normal distribution, Chi square, t and F. Distribution of statistics based on ordered samples. Asymptotic sampling distributions. Introduction to multivariate normal distribution and linear models. Sufficient statistics, maximum likelihood, least squares, linear estimation, other methods of point estimation, and discussion of their properties, Cramer-Rao inequality and Rao-Blackwell theorem. Tests of statistical hypotheses, simple and composite hypotheses, likelihood ratio tests, power of tests. Typically offered Fall Spring. 3.000 Credit Hours Levels: Graduate, Professional, Undergraduate Schedule Types: Lecture College of Science College Statistics Department Course Attributes: CH Technical Electives, Upper Division
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3.00 Credits
Credit Hours: 3.00. Bayesian and decision theoretic formulation of problems; construction of utility functions and quantifications of prior information; methods of Bayesian decision and inference, with applications; empirical Bayes; combination of evidence; Bayesian design and sequential analysis; comparisons of statistical paradigms. Typically offered Summer. 3.000 Credit Hours Levels: Graduate, Professional, Undergraduate Schedule Types: Lecture College of Science College Statistics Department Course Attributes: CH Technical Electives, Upper Division
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3.00 Credits
Credit Hours: 3.00. Topics include multivariate normal distribution theory, distributions of quadratic forms, properties of the normal linear model with applications to regression and the analysis of variance, asymptotic distributions of maximum likelihood estimators and likelihood ratio tests, sequential probability ratio test, distributions of order statistics with applications to nonparametric inference and rank tests. Typically offered Fall Spring Summer. 3.000 Credit Hours Levels: Graduate, Professional, Undergraduate Schedule Types: Lecture Regional Campus Only College Course Attributes: Credit By Exam
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3.00 Credits
Credit Hours: 3.00. (MA 532) A basic course in stochastic models, including discrete and continuous time Markov chains and Brownian motion, as well as an introduction to topics such as Gaussian processes, queues, epidemic models, branching processes, renewal processes, replacement, and reliability problems. Typically offered Spring. 3.000 Credit Hours Levels: Graduate, Professional, Undergraduate Schedule Types: Lecture College of Science College Statistics Department Course Attributes: CH Technical Electives, Upper Division
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3.00 Credits
Credit Hours: 3.00. Binomial test for dichotomous data, confidence intervals for proportions, one-sample signed Wilcoxon Rank test, 2-sample Wilcoxon test, 2-sample Rank tests for dispersion, Kruskal-Wallis test for one-way layout, Friedman Rank test for 2-way layout. Kendall distribution-free test for independence, one and two sample Kolmogorov-Smirnov tests, nonparametric regression. Typically offered Summer Fall Spring. 3.000 Credit Hours Levels: Graduate, Professional, Undergraduate Schedule Types: Lecture Regional Campus Only College
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3.00 Credits
Credit Hours: 3.00. Deals with the modern statistical methods for analyzing time-to-event data. Background theory is provided, but emphasis is on applications and interpretations of results. Provides coverage of survivorship functions and censoring patterns; parametric models and likelihood methods, special life-time distributions; nonparametric inference, life-tables, estimation of cumulative hazard functions, the Kaplan-Meier estimator; one and two-sample nonparametric tests for censored data; semiparametric proportional hazards regression (Cox Regression), parameters' estimation, stratification, model fitting strategies, and model interpretations. Heavy use of statistical software, such as S-Plus and SAS. Typically offered Summer Fall Spring. 3.000 Credit Hours Levels: Graduate, Professional, Undergraduate Schedule Types: Lecture Regional Campus Only College
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3.00 Credits
Credit Hours: 3.00. (MA 538) Mathematically rigorous, measure-theoretic introduction to probability spaces, random variables, expectation, independence, weak and strong laws of large numbers, conditional expectations, and martingales. Typically offered Spring. 3.000 Credit Hours Levels: Graduate, Professional, Undergraduate Schedule Types: Lecture College of Science College Statistics Department Course Attributes: CH Technical Electives, Upper Division
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3.00 Credits
Credit Hours: 3.00. (MA 539) Convergence of probability laws; characteristic functions; convergence to the normal law; infinitely divisible and stable laws; Brownian motion and the invariance principle. Typically offered Fall. 3.000 Credit Hours Levels: Graduate, Professional, Undergraduate Schedule Types: Lecture College of Science College Statistics Department Course Attributes: CH Technical Electives, Upper Division
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3.00 Credits
Credit Hours: 3.00. An introduction to the mathematical tools and techniques of modern finance theory, in the context of Black-Scholes option pricing. Brownian motion and its stochastic calculus, Ito's formula, and Feynman-Kac formula. Pricing and hedging of claims on Black-Scholes assets. Incomplete markets. Path-dependent options. Stochastic portfolio optimization. Typically offered Spring. 3.000 Credit Hours Levels: Graduate, Professional, Undergraduate Schedule Types: Lecture College of Science College Statistics Department Course Attributes: CH Technical Electives, Upper Division
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