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Course Criteria
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3.00 Credits
Zhao. Prerequisite(s): MATH 114 or equivalent and an undergraduate introduction to probability and statistics. This is primarily a statistical methodology course. Regression based techniques dominate the course, including the simple linear model, multiple regression, ANOVA and ANCOVA, and related models for discrete choice data, including logistic regression. Regression techniques for Panel Data will also be presented. Real data sets will be used to demonstrate the methods and clarify the use of the techniques. At the end of the semester students are expected to be able to analyze a data set (quantitative or qualitative) wisely and conclude correctly. We will start the course with a very quick introductory review of basis statistical techniques needed such as the central limit theorem, confidence intervals and hypotheses tests. R and JMP will be used throughout the lectures.
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3.00 Credits
Small. Prerequisite(s): STAT 520. This is a graduate course in econometrics for applied economics graduate students. The goal of the course is to prepare students for empirical research by investigating several important econometric methods.
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3.00 Credits
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.
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3.00 Credits
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.
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3.00 Credits
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.
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3.00 Credits
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.
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3.00 Credits
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.
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3.00 Credits
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.
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3.00 Credits
Staff. Prerequisite(s): STAT 550 and 551. A continuation of STAT 550.
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3.00 Credits
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.
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