[Forum SIS] UNIBO Statistics Seminars

[Silvia Cagnone]

We are glad to announce the following  Statistics Seminars:


Wednesday, September 26, 4 pm

Room III, via delle Belle Arti 41, Bologna




Małgorzata Bogdan
(University of Breslavia)


Sorted L-One Penalized Estimation

Abstract:

Sorted L-One Penalized Estimation (SLOPE) is a relatively new method of convex optimization for solving problems related to the analysis of Large Data Sets. In this talk we will introduce the method and give an overview of current theoretical results and applications for different statistical problems including the robust regression, gaussian graphical models and portfolio optimization.





Thursday, September 27, 2.15 pm

Room III, via delle Belle Arti 41, Bologna



 Jeanine Houwing-Duistermaat

(University of Leeds)




Partial Least Square methods for omics data sets

Abstract:



The availability of large omics datasets in epidemiological and clinical studies provides many opportunities for research in statistical bioinformatics. The hope is that the abundance of information will provide better understanding of underlying disease mechanisms and accurate prediction models enabling patient targeted screening and treatment. Statistical challenges are to deal with data cleaning, heterogeneity across omic datasets, high dimensionality, data integration and the presence of high correlation within and between datasets (Morris et al, 2017; Houwing- Duistermaat et al, 2017). In this talk I will present Partial Least Squares (PLS) methods for multivariate regression and for data integration and dimension reduction when analysing several omics datasets simultaneously.

Three PLS type of methods for omics analysis will be considered namely the standard PLS algorithm (Wold, 1972), Envelope (Cook et al, 2015) and our recently developed Probabilistic PLS (PPLS) (Bouhaddani et al, 2018). Envelope and PPLS are maximum likelihood methods. PLS and PPLS can deal with high dimensions while Envelope requires n larger than p. PPLS maximizes a constrained log likelihood to ensure that the solution is unique. The methods will be illustrated with several data examples. The results of simulation studies to compare their performances will be shown.





Contact person: Angela Montanari


The schedule of the statistics seminars are available at http://www.stat.unibo.it/it/dipartimento/seminari-di-statistica-2018

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