Courses Taught by Peter Xuekun Song

BIOSTAT802 ADVANCED INFERENCE II

  • Graduate level
  • Winter term(s)
  • 3 Credit Hour(s)
  • Instructor(s): Song, Peter Xuekun
  • Prerequisites: Biostat 601, Biostat 602, and MATH 451 or equivalent
  • Description: This sequence 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.

BIOSTAT885 Nonparametric Statistics

  • Graduate level
  • Winter term(s)
  • 3 Credit Hour(s)
  • Instructor(s): Song, Peter Xuekun
  • Prerequisites: Biostat 601/602 or Perm. Instr.
  • Description: Theory and techniques of nonparametrics and robustness. M-estimation, influence function, bootstrap, jackknife, generalized additive models, smoothing techniques, penalty functions, projection pursuit, CART.
  • Syllabus for BIOSTAT885

BIOSTAT895 Analysis of Multivariate Categorical Data

  • Graduate level
  • Fall term(s)
  • 3 Credit Hour(s)
  • Instructor(s): Song, Peter Xuekun
  • Not offered 2017-2018
  • Prerequisites: Biostat 651 and Biostat 695 or Perm. Instr.
  • Description: Probability models for two-way tables; multi-factor, multi-response framework; product multinomial distribution theory; Taylor series estimates of variance, weighted least squares and Wald statistics; constraint equations; models for characterizing interactions; step-wise variable selection; factorial designs with multinomial responses; repeated measurement experiments; log-linear models; paired-choice and bioassay experiments; life-table models.