BPSC148

TITLE: Quantitative Genetics

Syllabus

  1. Introduction
  2. Chapter: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
  3. Appendix: A B C D E F G H I

STAT288

TITLE: Literature Seminar

Monday 9:10 - 10:00
STAT 2600

CATALOG DESCRIPTION:

STAT 288 Literature Seminar 1 Seminar, 1 hour. Students will make oral presentations summarizing important research papers in the statistics literature. All graduate students are encouraged to participate. Topics may vary each term. Graded Satisfactory (S) or No Credit (NC).

List of Suggested Papers for Discussion

  1. Dempster, A.P., N.M. Laird, and D.B. Rubin. 1977. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B 39: 1-38.
  2. Gelman, A. 2005. Analysis of variance - Why it is more important than ever. The Annals of Statistics 33: 1-53.
  3. George, E.I. and R.E. McCulloch. 1993. Variable selection via Gibbs sampling. Journal of the American Statistical Association 88: 881-889.
  4. Henderson, C.R. 1950. Estimation of genetic parameters (Abstract). The Annals of Mathemetical Statistics 21: 309.
  5. Henderson, C.R. 1975. Best linear unbiased estimation and prediction under a selection model. Biometrics 31: 423-447.
  6. Henderson, C.R., O. Kempthorne, C.M. Searle, and C.M. von Krosigk. 1959. The estimation of environmental and genetic treands from records subject to culling. Biometrics 15: 192-218.
  7. Hoerl, A.E. and R.W. Kennard. 1970a. Ridge regression: applications to nonorthogonal problems. Technometrics 12: 69-82.
  8. Hoerl, A.E. and R.W. Kennard. 1970b. Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12: 55-67.
  9. Hoerl, A.E. and R.W. Kennard. 1976. Ridge regression iterative estimation of the biasing parameter. Communications in Statistics - Theory and Methods 5: 77-88.
  10. Ishwaran, H. and J.S. Rao. 2005. Spike and slab variable selection: frequentist and Bayesian strategies. The Annals of Statistics 33: 730-773.
  11. Lindley, D.V. and A.F.M. Smith. 1972. Bayes estimates for the linear model. Journal of the Royal Statistical Society, Series B 34: 1-41.
  12. Louis, T. 1982. Finding the observed information matrix when using the EM algorithm. Journal of the Royal Statistical Society, Series B 44: 226-233.
  13. McCulloch, R.E. 1994. Maximum likelihood variance components estimation for binary data. Journal of the American Statistical Association 89: 330-335.
  14. Robinson, G.K. 1991. That BLUP is a good thing: the estimation of random effects. Statistical Science 6: 15-32.
  15. Roth, V. 2004. The generalized LASSO. IEEE Transaction Neural Networks 15: 16-28.
  16. Tibshirani, R. 1996. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society, Series B 58: 267-288.

BPSC234

TITLE: Staitstical Genomics