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Graduate Course Schedules

 

Graduate Course Descriptions

  • BST 401 Probability Theory
  • BST 402 Stochastic Processes
  • BST 411 Statistical Inference
  • BST 412 Large-Sample Theory and Methods
  • BST 413 Bayesian Inference
  • BST 416 Applied Statistics
  • BST 421 Sampling Theory
  • BST 422 Design of Experiments
  • BST 426 Linear Models
  • BST 441 Applied Multivariate Analysis
  • BST 450 Data Analysis
  • BST 451 Exploratory Data Analysis
  • BST 452 Design of Experiments
  • BST 463 Introduction to Biostatistics
  • BST 464 Applied Linear Regression
  • BST 465 Design of Clinical Trials
  • BST 466 Categorical Data Analysis
  • BST 470 Internship/Applied Project
  • BST 476 Introduction to Linear Models
  • BST 477 Introduction to Statistical Software I
  • BST 478 Introduction to Statistical Software II
  • BST 479 Generalized Linear Models
  • BST 491 Reading Course at the Master's Level
  • BST 495 Research at the Master's Level
  • BST 497 Seminar in Statistical Literature
  • BST 511 Topics in Statistical Inference I
  • BST 512 Topics in Statistical Inference II
  • BST 513 Analysis of Longitudinal and Dependent Data
  • BST 514 Survival Analysis
  • BST 520 Current Topics in Bioinformatics
  • BST 525 Introduction to Health Informatics
  • BST 531 Nonparametric Inference
  • BST 536 Sequential Analysis
  • BST 541 Multivariate Analysis
  • BST 550 Topics in Data Analysis
  • BST 570 Topics in Biostatistics
  • BST 582 Introduction to Statistical Consulting
  • BST 590 Supervised Teaching
  • BST 591 Reading Course at the Ph.D. Level
  • BST 592 Supervised Statistical Consulting
  • BST 595 Research at the Ph.D. Level

Full Descriptions

BST 401 Probability Theory

Prerequisites:
MTH 265 or equivalent (or permission)
Description:
Probability spaces; random variables; independence; distributions; expectation; characteristic functions and inversion theorems; convergence; laws of large numbers; central limit theorem.
Offered:
Fall
Updated:
04/04/06

BST 402 Stochastic Processes

Prerequisites:
BST 401
Description:
Markov chains; birth-death processes; random walks; renewal theory; Poisson processes; Brownian motion; branching processes; martingales; with applications.
Offered:
Fall
Updated:
04/04/06

BST 411 Statistical Inference

Prerequisites:
STT 203 and MTH 265 or equivalent
Description:
Probability distributions, transformations and sampling distributions; statistical models; estimation, hypothesis testing, and confidence intervals for parametric models; introduction to large-sample methods
Offered:
Fall
Updated:
04/04/06

BST 412 Large-Sample Theory and Methods

Prerequisites:
BST 401 and BST 411
Description:
Weak convergence; asymptotic linearity; local analysis; large sample estimation, maximum likelihood estimation and M-estimation; Wald, likelihood ratio, and score tests; confidence regions; nuisance parameters; efficiency; multinomial chi-square tests.
Offered:
Spring
Updated:
04/04/06

BST 413 Bayesian Inference

Prerequisites:
BST 411
Description:
Introduction to statistical decision theory; loss functions; admissibility; Bayes and empirical Bayes procedures; hierarchical models; noninformative and informative prior distributions; modern numerical techniques, including Markov chain Monte Carlo; model checking; posterior predictive checks; sensitivity analysis.
Offered:
Fall
Updated:
04/04/06

BST 416 Applied Statistics

Prerequisites:
STT 211 or STT 212 or BST 463 or equivalent
Description:
One- and two-way analysis of variance; simple and multiple regression; analysis of covariance; analysis of residuals, use of transformations; topics from contingency table analysis and nonparametric statistics. Emphasis on real examples from the biomedical and social sciences, with extensive use of statistical software.
Offered:
Spring
Updated:
04/04/06

BST 421 Sampling Theory

Prerequisites:
STT 213 or STT 203
Description:
Sampling designs; theories of inference in finite populations; sampling with varying probabilities; stratified, systematic, multistage and multiphase sampling; estimation based on ratio and regression methods.
Offered:
Fall
Updated:
04/04/06

BST 422 Design of Experiments

Prerequisites:
BST 416 or BST 464 or BST 476
Description:
Basic designs and their principles; randomization; blocking; use of concomitant information.
Offered:
Spring
Updated:
04/04/06

BST 426 Linear Models

Prerequisites:
STT 203 and MTH 235
Description:
Theory of least-squares; point estimation in the general linear model; projection operators, estimable functions and generalized inverses; tests of general linear hypotheses; power; confidence intervals and ellipsoids; simultaneous inference; linear and polynomial regression; analysis of variance and analysis of covariance models; fixed, random, and mixed effects; correlation; prediction.
Offered:
Spring
Updated:
04/04/06

BST 441 Applied Multivariate Analysis

Prerequisites:
BST 476 or BST 426
Description:
Methodology and applications of multivariate analysis; Hotelling's T2; multivariate regression and analysis of variance; classification and discrimination; principal components, clustering, and multidimensional scaling; use of statistical software.
Offered:
Spring
Updated:
04/04/06

BST 450 Data Analysis

Prerequisites:
BST 426 and BST 477 or BST 478
Description:
Statistical analysis of data under nonstandard conditions; examination of adequacy of model assumptions; goodness-of-fit testing; transformations; robust inference.
Updated:
04/04/06

BST 451 Exploratory Data Analysis

Prerequisites:
BST 416 or BST 476 and BST 478
Description:
Graphical techniques to reveal structure in data; model fitting to describe structure; model checking; transformations; outliers and resistant fitting methods.
Updated:
04/04/06

BST 452 Design of Experiments

Prerequisites:
BST 426 and BST 477 or BST 478
Description:
Completely randomized designs; replication; covariate adjustment; randomized block designs; fixed vs. random effects; Latin and Graeco-Latin squares; confounding; nesting; factorial and fractional factorial designs; split-plot designs; incomplete block designs; response surfaces.
Updated:
04/04/06

BST 463 Introduction to Biostatistics

Restrictions:
Permission of instructor required for undergraduates
Description:
Introduction to statistical techniques with emphasis on applications in the health sciences. Summarizing and displaying data; introduction to probability; Bayes' theorem and its application in diagnostic testing; binomial, Poisson, and normal distributions; sampling distributions; estimation, confidence intervals, and hypothesis testing involving means and proportions; simple correlation and regression; contingency tables; use of statistical software.
Offered:
Fall
Updated:
04/04/06

BST 464 Applied Linear Regression

Restrictions:
Permission of instructor required for undergraduates
Prerequisites:
BST 463 or equivalent
Description:
One-way and two-way analysis of variance; multiple comparisons involving means; fixed and random effects; simple and multiple linear regression; analysis of covariance; interactions; correlation and partial correlation; multicollinearity; model selection; model checking.
Offered:
Fall
Updated:
04/04/06

BST 465 Design of Clinical Trials

Restrictions:
Permission of instructor required for undergraduates
Prerequisites:
BST 463 or equivalent
Description:
Introduction to the principles of clinical trials; clinical trial protocols; overview of the drug development process; hypotheses/objectives; specification of response variables; defining the study population; randomization; blinding; ethical issues; factorial designs; crossover designs; equivalence trials; trial monitoring and interim analyses; sample size and power; data collection and management; issues in data analysis and reporting; evaluating clinical trial reports.
Offered:
Spring
Updated:
04/04/06

BST 466 Categorical Data Analysis

Restrictions:
Permission of instructor required for undergraduates
Prerequisites:
BST 464 or equivalent
Description:
Chi-square tests for independence; mutual, partial, and conditional independence; Cochran-Mantel-Haenszel methods; loglinear models for analysis of two-way and three-way contingency tables; logistic regression; Poisson regression; models for nominal and ordinal categorical responses; analysis of matched-pair categorical data; “exact” methods for inference; interactions; goodness-of-fit; model checking; introduction to survival analysis, including Kaplan-Meier curves and the Cox proportional hazards regression model.
Offered:
Spring
Updated:
04/04/06

BST 470 Internship/Applied Project

Description:
As required for completion of the M.S. degree in medical statistics, the student works on a medical research project under the guidance of department faculty or under supervision in an industrial setting. The student should have contact with medical investigators as well as statisticians. The work should be coherently summarized in a written document. Oral presentation of the work is required.
Offered:
Summer
Updated:
04/04/06

BST 476 Introduction to Linear Models

Prerequisites:
STT 203 or STT 212 or BST 463
Description:
Simple and multiple regression models; least-squares estimation; hypothesis testing; interval estimation; prediction; matrix formulation of the general linear model; polynomial regression; analysis of variance; analysis of covariance; methods for simultaneous inference; residual analysis and checks of model adequacy.
Offered:
Fall
Updated:
04/04/06

BST 477 Introduction to Statistical Software I

Restrictions:
Open only to graduate students in offering department
Prerequisites:
STT 212 or BST 463
Description:
Introduction to a statistical software package. The software to be introduced may vary from semester to semester; a common choice is SAS. Generally offered during the first 6 weeks of the summer. Credit - none.
Updated:
04/04/06

BST 478 Introduction to Statistical Software II

Restrictions:
Open only to graduate students in offering department
Prerequisites:
STT 212 or BST 463
Description:
Introduction to a statistical software package. The software to be introduced may vary from semester to semester; a common choice is S-Plus. Generally offered during the first 6 weeks of the summer. Credit - none.
Updated:
04/04/06

BST 479 Generalized Linear Models

Prerequisites:
BST 411 and BST 426
Description:
Generalized linear models; computational techniques for model fitting; logistic and conditional logistic regression; log-linear models; models for nominal and ordinal categorical data; quasi-likelihood functions; model checking; introduction to semiparametric generalized linear models.
Offered:
Fall
Updated:
04/04/06

BST 491 Reading Course at the Master's Level

Description:
Credit varies
Updated:
04/04/06

BST 495 Research at the Master's Level

Description:
Credit varies
Updated:
04/04/06

BST 497 Seminar in Statistical Literature

Description:
1 credit
Offered:
Fall and Spring
Updated:
04/04/06

BST 511 Topics in Statistical Inference I

Prerequisites:
Varies by topic
Description:
Advanced topics in statistical inference and/or decision theory. Recent topics include:
  • Bayesian Inference
  • Smoothing Methods
  • Frailty Models
  • Permutation Tests
  • The Bootstrap, The Jackknife, and Resampling Methods
    • Offered:
    Fall
    Updated:
    04/04/06

    BST 512 Topics in Statistical Inference II

    Prerequisites:
    Varies by topic
    Description:
    Advanced topics in statistical inference and/or decision theory. Recent topics include:
  • Bayesian Inference
  • Smoothing Methods
  • Frailty Models
  • Permutation Tests
  • The Bootstrap, The Jackknife, and Resampling Methods
    • Offered:
    Spring
    Updated:
    04/04/06

    BST 513 Analysis of Longitudinal and Dependent Data

    Prerequisites:
    BST 401 and BST 411 and BST 426
    Description:
    Modern approaches to the analysis of longitudinal and dependent data; random and mixed effects models; marginal models; generalized estimating equations; models for continuous and discrete outcomes.
    Offered:
    Spring
    Updated:
    04/04/06

    BST 514 Survival Analysis

    Prerequisites:
    BST 411 and BST 412 or BST 402
    Description:
    Parametric, nonparametric, and semiparametric methods for the analysis of survival data. Right censoring; Kaplan-Meier curves; log-rank and weighted log-rank tests; survival distributions; accelerated life and proportional hazards regression models; time-dependent covariates; partial likelihood; models for competing risks and multiple events.
    Offered:
    Fall
    Updated:
    04/04/06

    BST 520 Current Topics in Bioinformatics

    Description:
    Basic concepts of modern molecular biology; bioinformatics technologies; sequence analysis of nucleic acids and proteins (methods of sequence alignment and associated search algorithms); prediction of structure and functions: protein folding and RNA secondary structure; statistical methods for microarray gene expression data analysis: (1) univariate methods for selecting differentially expressed genes (SAM, step-down and step-up resampling methods, empirical Bayes method) and (2) multivariate methods for identifying subsets of differentially expressed genes and pathway recognition (distance-based and error-based approaches, successive selection of subsets of genes, testing significance in multivariate settings); selection bias in multivariate analysis and cross-validation of classification rules; Support Vector Machines in the analysis of microarrays; unsupervised learning with microarray data; identification of gene regulatory networks from gene perturbation experiments; prognostic value of molecular signatures of cancer cells; common pitfalls in gene expression data analysis and a critical overview of the existing methods; methods for analysis of complex genetic traits and gene finding in genetic epidemiology; promising avenues for future statistical research in the field of bioinformatics.
    Offered:
    Spring
    Updated:
    04/04/06

    BST 525 Introduction to Health Informatics

    Restrictions:
    Permission of instructor required
    Prerequisites:
    Health sciences (medical, nursing, public health, etc.) background or technical (computer science, information science, statistics, etc.) background
    Description:
    Introduction to health informatics; clinical data and biomedical knowledge; electronic medical records and integrated health care information systems; standards for health information technology; natural language and text processing/information retrieval; human factors in health informatics; translational informatics and decision support systems; public health informatics, telemedicine, and patient monitoring; evaluation of health care information systems; consumers, web, and health education.
    Updated:
    04/04/06

    BST 531 Nonparametric Inference

    Prerequisites:
    BST 411
    Description:
    Statistical procedures based on ranks, order statistics, signs, permutations, and runs; tests for randomness, symmetry, and independence; invariance considerations and optimality; treatment of ties; distributional problems and asymptotic theory; U-statistics; Chernoff-Savage theorem; robustness and efficiency.
    Updated:
    04/04/06

    BST 536 Sequential Analysis

    Prerequisites:
    BST 412
    Description:
    The Wald sequential probability ratio test and generalizations; tests of composite hypotheses; nonparametric sequential procedures; sequential estimation and confidence intervals; Brownian-motion based sequential methods, with applications to clinical trials; group sequential methods; optimal stopping rules.
    Updated:
    04/04/06

    BST 541 Multivariate Analysis

    Prerequisites:
    BST 411 and BST 426
    Description:
    Multivariate normal and Wishart distributions and associated distributions; estimation; invariance reduction; Hotelling?s T2; multivariate general linear model; simultaneous confidence bounds; step down procedures; optimality properties; classification; discrimination; principal components.
    Updated:
    04/04/06

    BST 550 Topics in Data Analysis

    Restrictions:
    Permission of instructor required
    Description:
    Advanced statistical methods for data analysis.
    Updated:
    04/04/06

    BST 570 Topics in Biostatistics

    Restrictions:
    Permission of instructor required
    Description:
    Advanced biostatistical techniques.
    Updated:
    04/04/06

    BST 582 Introduction to Statistical Consulting

    Description:
    Formal instruction on developing and managing consulting relationships.
    Updated:
    04/04/06

    BST 590 Supervised Teaching

    Description:
    One to two classroom hours per week of discussion and problem solving with University of Rochester students, under the guidance of a member of the faculty.
    Updated:
    04/04/06

    BST 591 Reading Course at the Ph.D. Level

    Description:
    Special work for doctoral candidates, arranged individually.
    Updated:
    04/04/06

    BST 592 Supervised Statistical Consulting

    Description:
    Supervised consulting with medical and other scientific researchers under the guidance of a member of the faculty.
    Updated:
    04/04/06

    BST 595 Research at the Ph.D. Level

    Description:
    Credit varies
    Updated:
    04/04/06

    Smoothing Methods (BST 511 and BST 512)

    Prerequisites:
    BST 412, BST 426, and BST 478
    Description:

    The course will cover 3 main topics: (1) density estimation methods, including histograms, frequency polygons, kernel density estimators, local likelihood density estimators, and penalized likelihood and spline-based estimators; (2) nonparametric regression, with an emphasis on local polynomial modeling and some discussion of cubic smoothing splines; and (3) generalized additive models (GAM). Attention will be paid to locally varying smoothing parameters, boundary effects and corrections, consistency, rates of convergence, extensions to higher dimensions and the "curse of dimensionality," limit distributions, smoothing parameter selection, and kernel choice, including higher-order and equivalent kernels.

    Frailty Models in Survival Analysis (BST 511 and BST 512)

    Prerequisites:
    BST 411, BST 479 or BST 514
    Description:
    The notion of frailty provides a convenient way to introduce random effects, association and unobserved heterogeneity into models for survival data. In its simplest form a frailty is an unobserved random proportionality factor which modifies the hazard function of an individual, or of related individuals. In essence the concept goes back to the 1920 work of Greenwood and Yule on "accident proneness." The term frailty itself was introduced by Vaupel, Manton and Stallard in 1979 and applications in survival analysis were popularized in a series of papers by P. Hougaard. Applications to multivariate survival data date from a seminal 1978 paper by D. Clayton.

    The course will provide a general introduction to frailty models for univariate and multivariate survival data and for repeated events. We will discuss the formulation of frailty models and identifiability aspects, connections with two-sample rank tests for survival data and with measures of association for bivariate survival data. Parametric and semiparametric methods of fitting frailty models will be reviewed, including the use of the EM algorithm and related approaches including hierarchical likelihood. Extensions to correlated frailty models will also be described. The distinction between "conditional models," in which the effect of observed covariates is described conditionally on the value of the random effect and "marginal models," which integrate over the unobserved effect, will be emphasized.

    Classical survival analysis methodology, including the Kaplan-Meier estimator and Cox's regression model, will be reviewed briefly at the start of the course.

    The Bootstrap, The Jackknife, and Resampling Methods (BST 511 and BST 512)

    Prerequisites:
    BST 412 and BST 478
    Description:
    Bootstrap methods, including the nonparametric, parametric, smoothed, m-out-of-n, and randomly weighted bootstrap procedures, will be presented for estimating standard errors, constructing various types of confidence intervals, performing hypothesis tests, and estimating bias. One- and two-sample problems, regression, correleted data, and more general inference problems will be considered. Jackknife and cross-validation methods will also be discussed.
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