Online Degrees and Courses

Statistics Courses
20000. Elementary Statistics

22000. Statistical Methods and Their Applications

22200. Linear Models and Experimental Design

22400. Applied Regression Analysis

22600. Analysis of Qualitative Data

22700. Biostatistical Methods

24000. Probability and Statistics in the Natural Sciences

24100. Probability and Statistics in the Natural Sciences (50 unit course)

24200. Applied Probability and Stochastic Models

23400, 23500 Statistical Models and Methods I, II

24400, 24500, 24600. Statistical Theory and Methods

25100. Introduction to Mathematical Probability

26700. History of Statistics
Development of probability theory and its use in science to quantify uncertainty in observational data and as a conceptual framework for scientific theories.
Courses in the second list are offered each year or in alternate years and are intended,
primarily but not exclusively, for graduate students in statistics.

30100, 30200. Mathematical Statistics
The mathematical structure of statistics; parameter estimation, efficiency, confidence sets, tests of hypotheses, Bayesian analysis, decision theory and asymptotic methods.

30400. Distribution Theory
Deriving, characterizing, displaying and approximating distributions. Computer-based algebra; Edgeworth, saddlepoint, and Laplace approximations.

30700. Numerical Computation
Numerical algorithms, particularly in linear systems.

31200. Introduction to Stochastic Processes I
Branching processes, recurrent events, renewal theory, random walk, Markov chains, Poisson and birth-and-death processes.

31300. Introduction to Stochastic Processes II
Continuation of 31200, focusing on continuous time Markov chains, and martingales.

32000. Bayesian Statistics
Basic concepts and methods; recent advances in computational techniques: asymptotic approximation, Bayesian data analysis.

33100. Sample Surveys
Random sampling methods; stratification, cluster sampling, ratio estimation; methods for dealing with non-response and partial response.

34300. Applied Linear Statistical Methods
The theory, methods and applications of fitting and interpreting multiple regression models.

34500. Design and Analysis of Experiments
Linear models in experimental design: blocking, randomization, fractionation and confounding, fixed and random effects; analysis of designed experiments.

34700. Generalized Linear Models symmetric functions, Edgeworth and saddlepoint approximations.

32200. Applied Bayesian Data Analysis

32300. Analysis of Incomplete Data

33200. Survey Topics

33400. Applied Forecasting

33500. Time Series Analysis

33900. Spatial Statistics

34600. Multivariate Methodology and Data Analysis

34900. Applied Nonparametric Statistics

35000. Fundamentals of Epidemiology
Exponential-family models and variance functions; logistic regression, log-linear models. Quasi-likelihood, least squares and partially linear models.

36700. History of Statistics
Development of probability theory and its use in science to quantify uncertainty in observational data and as a conceptual framework for scientific theories.

38100–38300. Measure-Theoretic Probability
A rigorous treatment of probability including a development of measure theory: existence theorems, integration and expectation, characteristic functions, limit laws, Radon-Nikodym derivatives, conditional probabilities, martingales, Brownian Motion.

39000, 39100. Stochastic Calculus and Finance I, II
An introduction to probability theory and stochastic calculus integrated with the use of those tools for valuing and hedging derivative securities.

44100. Consulting in Statistics
The third list includes advanced or specialized courses that have been taught in the past years. Each year a selection similar to these are offered, depending on the current interests of the faculty and students.

30500. Theoretical Statistics
The foundations of statistical inference; the likelihood principle, sufficiency and conditionality principles, Bayesian and frequency-based inferences.

31400. Tensor Methods in Statistics
Multivariate moments and cumulants, set partitions and M^bius inversion, k-statistics and 35100. Advanced Epidemiology

35500. Statistical Genetics
Mapping of human disease genes and genetic markers; statistical and computational problems in the analysis of big pedigrees with complex genetic models.

35600. Introduction to Survival Analysis

35700. Resampling Methods
Bootstrapping, jackknifing and cross-validation; comparison to analytic techniques; applications.

36300. Topics in Likelihood Theory

36500. Statistical Decision Theory36900. Robust Estimation
The theory of M-estimators and linear functions of order statistics; history of robust inference, rejection of outliers, influence functions.

37500. Topics in Bayesian Analysis

37700. Simulation Methods
Random number generation, sampling from special distributions, Monte Carlo integration techniques, importance weighting, Gibbs sampling.

37800. Statistical Computation

37900. Computer Vision

38500. Advanced Probability: Stochastic Calculus

38600. Advanced Probability: Weak Convergence

38900. Probability and Finance

39200. Spectral Methods in Statistics
Fourier theory, computation, and applications to spectroscopy and imaging. Wavelets.

39300. Reading Fisher

39500. Nonparametric Regression and Classification

39600. Inference for Dependent Processes

39900. Master’s Seminar

45100. Workshop in Statistics

45500. Statistical Genetics (50 unit course)

45600. Workshop in Genetics

46100. Asymptotics in Inference (50 unit course)

46300. Topics in Statistical Inference (50 unit course)

47000. Conceptual Issues in Inference (50 unit course)

47800. Statistical Algorithms (50 unit course)

48800. Workshop on Shrinking Interval Asymptotics

49200. Wavelets (50 unit course)

49300. Workshop in Financial Engineering

49400. Workshop in Statistics and Finance

49500. Nonparametric Regression (50 unit course)

49700. The Craft of Research (50 unit course)

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