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Department: Biostatistics
Term: 2nd term
Credits: 4 credits
Contact: Vadim Zipunnikov
Academic Year: 2013 - 2014
Course Instructor:

Reviews key topics in modern applied statistics. Extends the topics of 140.755 to encompass generalized linear mixed effects models and introduces nonparametric smoothing, functional data analysis and data mining. Includes extensions of linear mixed effects to discrete outcomes, nonlinear and multivariate smoothing, semi-parametric models for clustered data, and statistical learning techniques such as classification, decision trees, and boosting. Emphasizes both rigorous methodological development and practical data analytic strategies. Presents computational methods designed for semi-parametric inference and discusses relevant software.

Learning Objective(s):
Upon successfully completing this course, students will be able to:
Give examples of different types of data arising in public health studies
Use modern statistical concepts such as Generalized Linear Mixed Models (GLMMs) for inference
Describe the relationship between GLMMs and linear mixed models
Extend models to account for clustering and correlation
Introduce nonparametric smoothing models
Describe modern statistical methods for complex datasets including functional data analysis and data mining
Apply theoretical concepts to scientific data using R and WinBUGS software
Improve computational and analytic skills through analysis of simulated data sets

Methods of Assessment: Homeworks and a final exam
Location: East Baltimore
Class Times:
  • Tuesday 10:30 - 11:50
  • Thursday 10:30 - 11:50
Lab Times:
  • Tu Th 10:00 - 10:20 (1)
Enrollment Minimum: 10
Instructor Consent: No consent required


Auditors Allowed: Yes, with instructor consent
Grading Restriction: Letter Grade or Pass/Fail