140.754.01 | AY 2013-2014 - 4th Term | 3 Credit(s)
TTh 10:30:00 AM
  • Contact Information
  • Course Learning Objectives

    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
  • Course Description

    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.

  • Intended Audience

    Biostatistics PhD students

  • Methods of Assessment

    Grading Policy: Student evaluation based on homework and a final exam.

    Grading Restrictions: Letter grade

  • Prerequisites


  • Academic Ethics Code

    The code, discussed in the Policy and Procedure Memorandum for Students, March 31, 2002, will be adhered to in this class:

    Students enrolled in the Bloomberg School of Public Health of The Johns Hopkins University assume an obligation to conduct themselves in a manner appropriate to the University's mission as an institution of higher education. A student is obligated to refrain from acts which he or she knows, or under the circumstances has reason to know, impair the academic integrity of the University. Violations of academic integrity include, but are not limited to: cheating; plagiarism; knowingly furnishing false information to any agent of the University for inclusion in the academic record; violation of the rights and welfare of animal or human subjects in research; and misconduct as a member of either School or University committees or recognized groups or organizations.

  • Disability Support Services

    If you are a student with a documented disability who requires an academic accommodation, please contact Betty H. Addison in the Office of Student Life Services:, 410-955-3034, or 2017 E. Monument Street.