BIOST 2038 - FOUNDATIONS OF STATISTICAL THEORY

Minimum Credits: 3
Maximum Credits: 3
The course covers basic theory of probability and statistical inference with a focus on appropriate use of standard methods and construction of new statistical inference tools. Topics covered in the first half include joint, marginal, and conditional probabilities; random variables and functions thereof; distribution characteristics of random variables; basic asymptotic theory and univariate theorems including Chebyshev's inequality, law of large numbers, and central limit theorem. Topics covered in the second half include principles and methods of constructing estimators (e.g., MLE, MME,CRLB), confidence intervals, and hypothesis testing (including Neyman-Person and Generalized Likelihood Ratio tests); data reduction principles and techniques, and their relationship to optimal statistical inference (e.g., sufficiency, Rao-Blackwell principle); basic likelihood-based, exact, conditional, and asymptotic statistical inference.
Academic Career: Graduate
Course Component: Lecture
Grade Component: Grad Letter Grade
Course Requirements: PLAN: Biostatistics (MS)

Click here for class schedule information.

Add to Portfolio.

Close Window