• Associate Professor of Statistics
• PhD Statistics, Stanford, 1988
• MS Statistics, Toronto
• BS Mathematics, Toronto

Photo by Peter Macdonald,
SSC Saskatoon, June 2005

Personal Webpage

Summary of research interests

Dr. Altman’s interest in statistics stems from her broad interests in the application of the mathematical sciences to problems in other disciplines – in particular, earth and environmental sciences, medical and biological sciences, and social sciences. Her statistical interests include bioinformatics, nonparametric smoothing, model selection and analysis of functional and longitudinal data. Dr. Altman’s current research is in bioinformatics and functional data analysis.

Dr. Altman’s bioinformatics work includes the design and analysis of microarray studies, functional genomics and gene clustering (by position on the chromosome, by sequence structure, and by function). Much of this work is currently in collaboration with the Floral Genome Project and with collaborators such as Claude dePamphilis, Hong Ma  and Iliana Baums.

Dr. Altman’s work in functional data analysis and nonparametric smoothing has focused on problems in which the errors are correlated, and parametric covariate effects are of interest. Current areas of interest include inference for self-modeling regression when curves are the response in a comparative experiment and fitting and inference for longitudinal and spatial data with a smooth component.

Dr. Altman has directed 14 MS theses and  co-directed 4 Ph.D. theses, as of 2008.

Stat 414  Introduction to Probability
Stat 511 Applied Linear Regression
Stat 512 Design of Experiments
Stat 540 Computationally Intensive Statistical Inference
Stat 597C Computing Environments for Statistics
Stat/Bio/CSE 598D Bioinformatics II - Microarrays
Stat/IBIOS 598A Current Research in Statistical Genomics

Talks and Lectures

Altman, N.S. and J. Villarreal. (2004). Self-modeling regression with random effects using penalized splines, Canadian Journal of Statistics, 32.jstor

Altman, N.S. (2000). Krige, smooth, both or neither? (with discussion). Australian and New Zealand Journal of Statistics 42: 441-461. Wiley

Altman, N.S. and C. Leger. (1997). On the optimality of prediction-based selection criteria and the convergence rates of estimators. Journal Royal Statistical Society, Series B 59: 205-216.jstor

Altman, N.S. and G. Casella.(1995) Nonparametric empirical Bayes growth curve analysis. JASA 90: 508-515. jstor

Léger, C. and Altman, N.S., (1993)  Assessing Influence in Variable Selection  Problems.  Journal of the American Statistical Association, 88, 547-556.jstor

Altman, N.S, (1990) Kernel Smoothing of Data with Correlated Errors.   Journal of the American Statistical Association, 85, 749-758   jstor

Han, B., Altman, N.S., Mong, J.A., Klein, L.C., Pfaff, D.W. and Vandenbergh, D. (2008) Comparing Quantitative Trait Loci and Gene Expression Data Associated with a Complex Trait, Advances in Bioinformatics : in press

P. Kerr Wall, Jim Leebens-Mack, Kai Müller, Dawn Field, Naomi S. Altman, Claude W. dePamphilis. (2007) PlantTribes:  A gene and gene family resource for comparative genomics in plants.  Nucleic Acid Research, 36, 970-976. NAR

Soltis D.E., H. Ma, M.W. Frohlich, P.S. Soltis, V.A. Albert, D.G. Oppenheimer, N.S. Altman, C.W. dePamphilis and J.H. Leebens-Mack. (2007) The floral genome: an evolutionary history of gene duplication and shifting patterns of gene expression. Trends in Plant Science 12(8):358-367.ScienceDirect

Altman, N.S., Hua, J. (2006) Extending the loop design for 2-channel microarray experiments Genetical Research, Vol 88, No. 3, p. 153-163.Cambridge

 Altman, N.S. (2005). Replication, variation and normalization in microarray experiments Applied Bioinformatics, 4, 33-44.pdf

Last updated: 5 Sept 2008