140.756.01 | AY 2013-2014 - 2nd Term | 4 Credit(s)
TTh 10:30:00 AM
  • Contact Information
    Vadim Zipunnikov
  • Course Learning Objectives

    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
    • Apply theoretical concepts to scientific data using R software
    • Improve computational and analytic skills through analysis of simulated data sets
  • Course Description

    Reviews key topics in modern applied statistics. Extends the topics of 140.755 to encompass generalized linear mixed effects models (GLMMs) and introduces semiparametric regression, nonparametric smoothing, and functional data analysis. Includes extensions of linear mixed effects to discrete outcomes, nonlinear and multivariate smoothing, semi-parametric models for clustered data. Emphasizes both rigorous methodological development and practical data analytic strategies. Presents computational methods designed for semi-parametric inference and discusses relevant software.

  • Intended Audience
    Biostatistics PhD students
  • Methods of Assessment

    Homework and a final project

  • Prerequisites
  • Course Schedule

    Please see the course Session for a full list of dates and items for this course.

  • Academic Ethics Code

    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.

  • Course Topics



    The course will cover the following topics:


    1. Generalized Linear Mixed Models

    2. Laplace approximation

    3. Penalized Quasi-Likelihood

    4. Expectation-Maximization (EM) and Majorize-Minimization (MM) algorithms

    5. Importance Sampling

    6. Quasi Monte Carlo (Quasi MC)

    7. MCEM

    8. Markov Chain Monte Carlo (Metropolis-Hastings and Gibbs sampling)

    9. Functional Data Analysis

  • Disability Support Services
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