Courses Taught by Alexander Tsodikov
BIOSTAT801: Advanced Inference I
- Graduate level
- Fall term(s)
- 3 Credit Hour(s)
- Instructor(s): Tsodikov, Alexander
- Prerequisites: Biostat 601, Biostat 602, and MATH 451 or equivalent
- Description: This is the first course of the sequence that covers advanced topics in probability theory, theory of point estimation, theory of hypothesis testing, and related large sample theory. This sequence replaces STAT 610/611 as biostatistics Ph.D. requirements.
- Course Goals: The goal of the sequence is to provide broad and deep theoretical training to Biostatistics Ph.D. students. Such training is essential for success in their thesis research and their future career.
- Competencies: The following competencies under Appendix 2.6.c in ``University of Michigan School of Public Health Self-Study -- Appendices" for Biostatistics PhD students are met: 2. Statistical techniques a. Advanced Mathematical Statistics b. Generalized Linear and Mixed Models c. Advanced Biostatistical Inference d. Stochastic Processes j. Bioinformatics and analysis of high-throughput biological data k. Survival analysis m. Bayesian inference techniques n. Nonparametric statistical methods 3. Mathematical foundation The graduate must acquire mathematical proficiency to be able to pursue theoretical development of statistical methods to address the needs of Biostatistical Inference.
- Syllabus for BIOSTAT801
|Department||Program||Degree||Competency||Specific course(s) that allow assessment||BIOSTAT||PhD||Apply the advanced probability theory and distribution theory||BIOSTAT801|