Biostat 251 - UCLA (Spring 2017)


  • Instructor : Donatello Telesca
  • Lecture: MW 10:00 am to 11:50 am- PUB HLT 41-268

News

Final Project - Due 6/14  (Final.pdf)


Homework


Schedule of Lectures

  • (4/03) Introduction - Multivariate Linear Model (Notes.pdf)
  • (4/05) Testing General Linear Hypotheses - Tests for Covariance Restrictions (Notes.pdf)
  • (4/10) Improved Estimation of Mean and Covariance - Bayesian Inference (Notes.pdf)
  • (4/12) Introduction to Longitudinal Data Analysis (Notes.pdf)
  • (4/17) Inference for LMEM (Notes.pdf)
  • (4/19) Generalized Linear Mixed Effects Models (Notes.pdf)
  • (4/24) Hierarchical Models (Notes.pdf)
  • (4/26) Marginal Models (GEE) (Notes.pdf)
  • (5/01) Linear Dimension Reduction - PCA (Notes.pdf, Notes.Rmd)
  • (5/03) Latent Variables - Factor Analysis and ICA (Notes.pdf, Notes.Rmd)
  • (5/08) Class Cancelled 
  • (5/10) Midterm
  • (5/15) Classification (Notes.pdf)
  • (5/17) Clustering (Notes.pdf)
  • (5/22) NP-Bayes (Notes.pdf)
  • (5/24) Graphical Models (Notes.pdf)
  • (5/29) No Lecture - Memorial day holiday
  • (5/31) Graphical Models (Note.pdf)
  • (6/05) Structure Discovery in Graphical Models (Notes.pdf)
  • (6/07) Copula Models

Coursework
6-7 HW Assignments 20%  
Midterm (05/08) 40%
Take-Home Final Due (06/14) 40%


Reading List

(Recommended Textbooks)

  • Anderson T.W., An Introduction to Multivariate Statistical Analysis. Wiley Series in Probability and Statistics. 
  • Izenman A.J., Modern Multivariate Statistical Techniques. Regression, Classification and Manifold Learning. Springer.
  • Seber G., Multivariate Observations. Wiley Series in Probability and Statistics.
  • Mardia, Kent and Bibby, Multivariate Analysis. Academic Press.
  • Rowe, Multivariate Bayesian Statistics. Chapman and Hall.
  • Diggle, Heagerty, Liang and Zeger, Analysis of Longitudinal Data. Oxford.
  • Hoff, P., A first Course in Bayesian Statistical Methods. Springer
  • Drton, M., Sturmfels, B and Sullivant, S. Lectures on Algebraic Statistics. Springer

 

(Longitudinal Data Analysis Textbooks)

  • Diggle, PJ, Heagerty, P, Liang KY and Zeger SL (2002). Analysis of Longitudinal Data. Oxford University Press.
  • Weiss R., Modeling Longitudinal Data. Springer Texts in Statistics.
  • Fitzmaurice GM, Laird, NM and Ware JH (2004). Applied Longitudinal Analysis. Wiley.
  • Verbeke, G and Molenberghs, G (2000) Linear Mixed Models for Longitudinal Data. Springer-Verlag.
  • Wakefield, J. (2012) Bayesian and Frequentist Regression Methods. Springer.


(Journal Articles)

  • Arnold, B.C. and Press, J. (1989). Compatible Conditional Distributions. JASA, 84, 152-156.
  • Gelman, A. and Meng, XL. (1991). A Note on Bivariate Distributions that Are Conditionally Normal. The American Statistician, 45 (2), 125-126.
  • Besag, J. (1974). Spatial Interaction and the Statistical Analysis of Lattice Systems. JRSS-B, 36, 192-236.  
  • Eaton, M.L. and Perlman, MD. (1973). Non-Singularity of Generalized Sample Covariance Matrices. Annals of Statistics, 1 (4), 710-717.
  • Okamoto, M. (1973). Distinctness of Eigenvalues of a Quadratic Form in a Multivariate Sample. Annals of Statistics, 1 (4), 763-765.
  • Jordan, M. I. (2004). Graphical Models. Statistical Science, 19 (1), 140-155.
  • Dawid, P. (1979). Conditional Independence in Statistical Theory. JRSS B, 41, 1, 1-31.
  • Lauritzen, S.L. and Spiegelhalter, D.J. (1988). Local Computations with Probabilities on Graphical Structures and their Application to Expert Systems. JRSS B, 50, 2, 157 - 224.
  • Liard, N.M. and Ware, F. (1982). Random Effects Models for Longitudinal Data, Biometrics, 38, 963-974.
  • Lindstrom, M., & Bates, D. (1988). Newton-Raphson and EM algorithms for linear mixed effects models for repeated measures data. JASA, 83,1014 1022.
  • Breslow, N.E. and Clayton, D.G. (1993). Approximate Inference in Generalized Linear Mixed Models. JASA, 88, 125-134.
  • Liang, K-Y. and Zeger S.L. (1986). Longitudinal Data Analysis Using Generalized Linear Models. Biometrika, 73, 12-22.
  • Sartori, N. and Severini, T.A. (2004). Conditional Likelihood Inference in Generalized Linear Mixed Models. Statistica Sinica, 14, 349-360.
  • Hoff, P.D. (2007) "Extending the rank likelihood for semiparametric copula estimation"  Annals of Applied Statistics, vol. 1 no. 1, 265-283.

Computing
Recommended computing for Biostat 251 will be based on the R programming language.
For more information please visit (http://cran.r-project.org/).