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Statistics500. (BSTA550, PSYC611) Applied Regression and Analysis of Variance. (A) Rosenbaum. Prerequisite(s): STAT 102 or 112 or equivalent. An applied graduate level course in multiple regression and analysis of variance for students who have completed an undergraduate course in basic statistical methods. Emphasis is on practical methods of data analysis and their interpretation. Covers model building, general linear hypothesis, residual analysis, leverage and influence, one-way anova, two-way anova, factorial anova. Primarily for doctoral students in the managerial, behavioral, social and health sciences. 501. (PSYC612) Introduction to Nonparametric Methods and Log-linear Models. (B) Rosenbaum. Prerequisite(s): STAT 102 or 112 or equivalent. An applied graduate level course for students who have completed an undergraduate course in basic statistical methods. Covers two unrelated topics: loglinear and logit models for discrete data and nonparametric methods for nonnormal data. Emphasis is on practical methods of data analysis and their interpretation. Primarily for doctoral students in the managerial, behavioral, social and health sciences. May be taken before STAT 500 with permission of instructor. 502. (EDUC683) Survey Methods and Design. (B) Boruch. Prerequisite(s): STAT 510 - 511. Methods and design of field surveys in education, the social sciences, criminal justice research, and other areas. It treats methods of eliciting information through household, mail, telephone surveys, methods of assuring privacy, enhancing cooperation rates and related matters. Fundamentals of statistical sampling and sample design are covered. Much of the course is based on contemporary surveys sponsored by the National Center for Education Statistics and other federal, state, and local agencies. 510. (BSTA620, STAT430) Probability. (A) Small. Prerequisite(s): A one year course in calculus. Probability. Elements of matrix algebra. Discrete and continuous random variables and their distributions. Moments and moment generating functions. Joint distributions. Functions and transformations of random variables. Law of large numbers and the central limit theorem. Point estimation: sufficiency, maximum likelihood, minimum variance. Confidence intervals. 511. Statistics. (B) Staff. Prerequisite(s): STAT 510. Tests of hypotheses. Examples of normal means and variances. Neyman-Pearson lemma. Generalized likelihood ratio tests. Ordinary least squares estimation. Inference in linear models: hypothesis tests and confidence statements. Bivariate normal distribution and correlation. Analysis of variance for one- and two-way layouts. Categorical data. Generalized least squares and autocorrelated disturbances. Lagged-variable models. Simultaneous equations models and introductory topics in econometrics. 512. (BSTA621, STAT432) Mathematical Statistics. (B) Staff. Prerequisite(s): STAT 430 or 510 or equivalent. An introductory course in the mathematical theory of statistics. Topics include estimation, confidence intervals, hypothesis testing, decision theory models for discrete data, and nonparametric statistics. 530. (MATH546) Probability. (A) Pemantle. Prerequisite(s): STAT 430 or 510 or equivalent. Measure theory and foundations of Probability theory. Zero-one Laws. Probability inequalities. Weak and strong laws of large numbers. Central limit theorems and the use of characteristic functions. Rates of convergence. Introduction to Martingales and random walk. 531. (MATH547) Stochastic Processes. (B) Pemantle. Prerequisite(s): STAT 530. Markov chains, Markov processes, and their limit theory. Renewal theory. Martingales and optimal stopping. Stable laws and processes with independent increments. Brownian motion and the theory of weak convergence. Point processes. 541. Statistical Methods. (A) Buja. Prerequisite(s): STAT 431 or 511 or equivalent. Multiple linear regression, logit and probit regression, analysis of variance, experimental design, log-linear models, goodness-of-fit. 542. Bayesian Methods and Computation. (B) Jensen. Prerequisite(s): STAT 430 or 510 or equivalent or permission of instructor. Sophisticated tools for probability modeling and data analysis from the Bayesian perspective. Hierarchical models, optimization algorithms and Monte Carlo simulation techniques. 550. (BSTA622) Mathematical Statistics. (A) Small. Prerequisite(s): STAT 431 or 511 or equivalent. Decision theory and statistical optimality criteria, sufficiency, invariance, estimation and hypothesis testing theory, large sample theory, information theory. 551. Introduction to Linear Statistical Models. (B) Brown. Prerequisite(s): STAT 550. Properties of the multivariate and spherical normal distributions, quadratic forms, estimation and testing in the linear model with applications to analysis of variance and regression models, generalized inverses, and simultaneous inference. 552. (BSTA820) Advanced Topics in Mathematical Statistics. (A) Staff. Prerequisite(s): STAT 550 and 551. A continuation of STAT 550. 553. Machine Learning. (B) Traskin. Prerequisite(s): STAT 510 and 512 or equivalent. This course gives a broad overview of the machine learning and statistical pattern recognition. Some topics will be rather glanced over while others will be considered in-depth. Topics include supervised learning (generative/discriminative models, parametric/nonparametric, neural networks, support vector machines, boosting, bagging, random forests), online learning (prediction with expert advice), learning theory (VC dimension, generalization bounds, bias/variance trade-off), unsupervised learning (clustering, k-means, PCA, ICA). Most of the course concentrates on the supervised and online learning. Faculty
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