BST 610. Introduction to Statistical Collaboration. 4 Credit Hours.

This course will introduce students to the art of statistical collaboration. The class will be highly interactive and will include an orientation to the process of statistical collaboration, individual and group assessment of the same consulting projects, and the completion of an individual "live" statistical collaboration project. For the final, live collaboration project, students will meet with the faculty and/or staff of the University of Miami who contributed thei r particular assigned consulting project(s). Students will need to research di fferent ststistical methods to find the best, feasible approach to address the particular consulting project. Students will present their plans for their own consulting projects in class and also help in the discussion/exploration of approaches for other student's consulting projects during class time. After finalization of the analysis plan the student will complete the statistical analyse s described within the plan and create a final report.
Components: LEC.
Grading: GRD.
Typically Offered: Fall & Spring.

BST 625. Survey of Statistical Computing. 3 Credit Hours.

This three credit course aims to familiarize students with the basic use of SAS and R for routine statistical analysis and prepare them for more advanced courses and/or thesis research. Statistical computation will be illustrated with examples in medical research, biological study and business. The focus of the course is on the computing environment, therefore a thorough discussion of statistical theories will not be provided. It is expected that students will already be prepared statistically.
Components: LEC.
Grading: GRD.
Typically Offered: Spring.

BST 630. Longitudinal and Multilevel Data. 3 Credit Hours.

This course offers students an introduction to linear and generalized linear models for the analysis of multi-level and longitudinal biomedical data. The course will also provide students with the opportunity to develop the skills necessary to perform analysis of these types of data using statistical software packages.
Components: LEC.
Grading: GRD.
Typically Offered: Fall.

BST 640. Modern Numerical Multivariate Methods. 3 Credit Hours.

This course covers multivariate topics from both a classical as well as modern perspective. Topics to include: Multivariate Normal Distribution; Spectral Decomposition; Principal Component Analysis; Canonical Correlation Analysis; Newton's Method; Steepest Descent; Gradient Boosting; Coordinate Descent Algorithms; Trees; Forests; Discriminant Analysis. The R programming language (http://www.r-project.org) will be used extensively throughout the course for computation and statistical analysis.
Components: LEC.
Grading: GRD.
Typically Offered: Spring.

BST 649. Advanced Individual Study. 1-3 Credit Hours.

Individual work on a special project under faculty guidance.
Components: LEC.
Grading: GRD.
Typically Offered: Fall, Spring, & Summer.

BST 650. Topics in Biostatistical Research. 1 Credit Hour.

The course consists of a series of research level presentations in contemporary biostatistics research (broadly defined) by diverse outside speakers as well as faculty in the Division of Statistics or in other units on campus who are hosting presentations in biostatistics research. The emphasis will be on new methodologies and new developments in existing methodologies. However, recent developments on the implementation and comparison of methodology and on data types may also be included.
Components: LEC.
Grading: SUS.
Typically Offered: Fall & Spring.

BST 660. Spatial Statistics. 3 Credit Hours.

Spatial data commonly arise from many fields including business, ecological, and health studies. This course will cover advanced techniques to tackle the spatial correlation. Topics include variogram estimation, spatial prediction, spatial point pattern analysis, estimating function based methods and others. Real data examples will be used to motivate and illustrate the use of the methods.
Components: LEC.
Grading: GRD.
Typically Offered: Fall.

BST 665. Design and Analysis of Clinical Trails. 3 Credit Hours.

This first part of this course is an advanced treatment of the key ideas undergirding the design and analysis of contemporary clinical trials. By the end of the course, students will have learned the statistical foundations of Phase I, II, and III trials from the standpoint of classical Frequentist, Bayesian, and adaptive designs. In addition, students will learn the usual mechanisms for preparing a clinical trial protocol, data safety and monitoring, interim analysis, and proper close out of a trial.
Components: LEC.
Grading: GRD.
Typically Offered: Fall.

BST 670. Bayes Data Analysis: Theory and Computing. 3 Credit Hours.

This first part of this course is an advanced and comprehensive treatment of the foundations of Bayesian theory. Beginning with the Bayesian alternative to sampling theory, the course covers Savage’“”•–—˜s axioms, the standard Bayesian inference procedure, subjective Bayes interpretation and prior selection, the minimax and complete class theorems, a variety of Bayesian principles (likelihood, stopping time etc.), and a selection of standard parametric and non-parametric examples. The second part focuses on the computational implementation of Bayesian inference, namely Gibbs sampling, Metropolis-Hastings, and other Markov-Chain-Monte Carlo techniques for obtaining the posterior and posterior quantities. Computational examples will be illustrated using R and WinBUGS. Special topics will include approximate Bayes computing (ABC) and particle methods.
Components: LEC.
Grading: GRD.
Typically Offered: Spring.

BST 675. Intermediate Probability. 3 Credit Hours.

This course covers the basic foundations of probability to limit theorems at anintermediate level. Topics to include are events, random variables and theirdistributions, discrete and continuous random variables, generating functions, markov chains and convergence.
Components: LEC.
Grading: GRD.
Typically Offered: Fall.

BST 680. Advanced Statistical Theory. 3 Credit Hours.

The first part of this course is a searching treatment of many of the key ideas undergirding hypothesis testing and estimation. In particular, several of the main theorems in mathematical statistics will be stated and proved in full detail. By the end of the course, students will have acquired enough background material for the treatment of a special topic, through a mix of lectures and assignments. Topics will include asymptotic expansions, information theory and non-parametrics.
Components: LEC.
Grading: GRD.
Typically Offered: Fall.

BST 690. Theory Of Survival Analysis. 3 Credit Hours.

Survival analysis is an important tool of statistic with many applications. In this course, without losing sight of such applications, we will give special emphasis to the probabilistic foundations, in terms of counting processes and martingales. Topics include: Failure time models, inference in parametric models, Cox models, counting processes and martingales, likelihood, competing risks and analysis of recurrent event data. The R programming language will be used.
Prerequisite: MTH 524, MTH 525, and BST 680.
Components: LEC.
Grading: GRD.
Typically Offered: Fall.

BST 695. Special Topics. 3 Credit Hours.

The course is designed to allow the listing of special topics within the Division of Biostatistics degree programs and cross list topics with other department's offerings.
Components: LEC.
Grading: GRD.
Typically Offered: Fall & Spring.

BST 698. Major Paper. 3 Credit Hours.

The student is working on their culminating project for the degree. The student is expected to explain a collection of related methods in some branch of statistics, use several of them to solve a motivated problem, explaining and contrasting the results. A faculty advisor and second reader are required.
Components: THI.
Grading: SUS.
Typically Offered: Fall, Spring, & Summer.

BST 699. Thesis Project. 1-6 Credit Hours.


Components: THI.
Grading: SUS.
Typically Offered: Fall, Spring, & Summer.

BST 830. Doctoral Dissertation (pre-candidacy). 1-12 Credit Hours.

Required of all candidates for the PhD. The student will enroll for credit as determined by his/her advisor but not for less than a total of 24. Not more than 12 hours of BST 730 may be taken in regular semester, nor more than six in a summer session.
Components: THI.
Grading: SUS.
Typically Offered: Fall, Spring, & Summer.

BST 840. Doctoral Dissertation (Post-Candidacy). 1-12 Credit Hours.

Required of all candidates for the PhD. The student will enroll for credits as determined by his/her advisor but not for less than a total of 24. Not more than 12 hours of BST 740 may be taken in regular semester, nor more than six in a summer session.
Components: THI.
Grading: SUS.
Typically Offered: Fall, Spring, & Summer.

BST 850. Research in Residence. 1 Credit Hour.

Used to establish research in residence for the PhD. after the student has been enrolled for the permissible cumulative total in appropriate doctoral research. Credit not granted. May be regarded as full-time residence as determined by the Dean of the Graduate School.
Components: THI.
Grading: SUS.
Typically Offered: Fall, Spring, & Summer.