Dr. Marina Bogomolov (Senior Lecturer)



Marina received a B.A. in Mathematics at the Technion in 2004, M.Sc. (cum laude) in Statistics at Tel-Aviv University in 2007, and Ph.D. in Statistics at Tel-Aviv University in 2012. She was a postdoctoral fellow in the Faculty of Industrial Engineering at the Technion during 2011–2012 and a postdoctoral fellow in the Department of Statistics at Haifa University during 2012–2013. She joined the Faculty of Industrial Engineering and Management of the Technion in 2013.

Selected Publications

Y. Benjamini and M. Bogomolov.  "Selective inference on multiple families of hypotheses," Journal of the Royal Statistical Society: Series B (2014), 76, Part 1, 297-318. DOI: 10.1111/rssb.12028

M. Bogomolov and R. Heller. "Discovering findings that replicate from a primary study of high dimension to a follow-up study."  Journal of the American Statistical Association (2013), 108:504, 1480-1492. DOI:10.1080/01621459.2013.829002.

R. Heller, M. Bogomolov, Y. Benjamini. "Deciding whether follow-up studies have replicated findings in a preliminary large scale “omics’” study," under revision in the Proceedings of the National Academy of Sciences of the United States of America.

Available at: http://arxiv.org/abs/1310.0606

Adaptive replicability analysis procedures that control the false discovery rate and the familywise error rate

(joint work with R. Heller, in preparation)

Order restricted univariate and multivariate inference with adjustment for covariates in partially linear models

(joint work with O. Davidov, in preparation)

Multiple testing procedures for genome-wide association studies with high-dimensional phenotypes

(joint work with C.B. Peterson, Y.Benjamini and C. Sabatti, in preparation)

Testing trees of families of hypotheses

(joint work with Y. Benjamini and C. Sabatti, in preparation)



Multiple comparisons Replicability analysis Order restricted inference

Development of multiple comparison procedures for selective inference.
Development of methods for replicability analysis—discovering findings that are replicated in two or more high-dimensional studies.
Applications in genomic studies.
Order-restrictive inference in semi-parametric models.

Contact Info

Room 304 Bloomfield Building