ADVANCED METHODS IN BIOSTATISTICS II Syllabus
Course Learning Objectives
Upon successfully completing this course, students will be able to:
- Apply the theories to standard experimental designs
- Discuss and estimate variance components
- Discuss theory and application of linear mixed models
- Discuss the concept of best linear unbiased estimation and prediction
- Develop the theory of restricted maximum likelihood
- Discuss shrinkage estimation
Course DescriptionSurveys basic statistical inference, estimates, tests and confidence intervals, and exploratory data analysis. Reviews probability distributions and likelihoods, independence and exchangeability, and modes of inference and inferential goals including minimizing MSE. Reviews linear algebra, develops the least squares approach to linear models through projections, and discusses connections with maximum likelihood. Covers linear, least squares regression, transforms, diagnostics, residual analysis, leverage and influence, model selection for estimation and predictive goals, departures from assumptions, efficiency and robustness, large sample theory, linear estimability, the Gauss Markov theorem, distribution theory under normality assumptions, and testing a linear hypothesis.
Intended AudienceBiostatistics PhD students
Methods of AssessmentStudent evaluation based on homework and a final exam.
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