Biostat 251 - UCLA (Spring 2019)


  • Instructor : Donatello Telesca

  • Lecture:

  • Office hours:


Announcements

 


Homework


Schedule of Lectures

  • (


Coursework
6-7 HW Assignments 20%  
Midterm (05/09) 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)

Mixed Effects Models

  • Sartori, N. and Severini, T.A. (2004). Conditional Likelihood Inference in Generalized Linear Mixed Models. Statistica Sinica, 14, 349-360.

  • Breslow, N.E. and Clayton, D.G. (1993). Approximate Inference in Generalized Linear Mixed Models. JASA, 88, 125-134.

  • 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.

 

GEE

  • Liang, K-Y. and Zeger S.L. (1986). Longitudinal Data Analysis Using Generalized Linear Models. Biometrika, 73, 12-22.

  • Prentice, R.L. (1988). Correlated binary regression with covariates specific to each binary observation. Biometrics, 44, 1033-1048.

  • Pepe, M. S. and Anderson, G.L. (1994) A cautionary note on inference for marginal regression models with longitudinal data and general correlated response data. Communications in Statistics - Simulation and Computation, 17, 1155-1171.

  • Zhao L. P. and Prentice R.L. (1990). Correlated binary regression using a quadratic exponential model. Biometrika, 77, 642-648.

 

Classic Multivariate Analysis

  • 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.

 

Graphical Models

  • 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.

  • 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.


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