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Department: Biostatistics
Term: 4th term
Credits: 3 credits
Contact: Jeffrey Leek
Academic Year: 2013 - 2014
Course Instructor:

Reviews key topics in modern applied statistics. Extends topics of 140.753 to encompass modern semi-parametric and non-parametric methods. Topics include linear, nonlinear and multivariate smoothing, semi-parametric models for clustered data, measurement error models, and statistical learning techniques such as classification, decision trees, and boosting. Emphasis is given both to rigorous methodological development and to practical data analytic strategies. Computational methods designed for semi-parametric inference are presented and relevant software is discussed.

Learning Objective(s):
Upon successfully completing this course, students will be able to:
Discuss modern regression tools such as scatterplot smoothing and additive models
apply additive models to public health studies and compare results with those of standard regression models
extend additive models to account for count outcomes and clustering
Discuss multivariate smoothing and applications to medical imaging and noise reduction
discuss measurement error models and their application to epidemiological studies
Discuss statistical and scientific model selection and uncertainty
apply modern statistical learning techniques such as clustering, classification trees and boosting for pattern recognition in complex data sets

Methods of Assessment: Student evaluation based on homework 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 - 10:30 (1)
Enrollment Minimum: 10
Instructor Consent: No consent required


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