یکشنبه, ۴ مهر ماه ۱۴۰۰
قالب وردپرس افزونه وردپرس

از سلسله وبینارهای دانشکده ریاضی دانشگاه صنعتی

عنوان:
Estimating sparse networks with hubs

سخنران:

آقای دکتر عباس خلیلی
عضو هیئت علمی دانشگاه مک گیل کانادا

زمان:

دوشنبه ۲۳ فروردین ۱۴۰۰

ساعت ۱۷

لینک ورود به وبینار:
https://meet.iut.ac.ir/b/ram-08w-sde-muf

َ

Abstract:

Graphical modelling techniques based on sparse estimation have been ap-

plied to infer large complex networks in many _elds, including biology and

medicine, engineering, _nance and social sciences. One structural feature of

some of the networks in such applications that poses a challenge for statis-

tical inference is the presence of a small number of strongly interconnected

nodes which are called hubs. For example, in microbiome research hubs or

microbial taxa play a signi_cant role in maintaining stability of the micro-

bial community structure. Methods based on L1- regularization have been

widely used for performing sparse estimation in the graphical modelling con-

text. However, while these methods encourage sparsity, they do not take into

account structural information of the network. In this talk, I will introduce

a new method for estimating networks with hubs that exploits the ability

of (inverse) covariance estimation methods to include structural information

about the underlying network. The proposed method is a weighted LASSO

approach with novel row/column sum weights, which we refer to as the hubs

weighted graphical LASSO. A practical advantage of the new method is that

it leads to an optimization problem that is solved using the e_cient graph-

ical lasso algorithm that is already implemented in the R package glasso. I

will also discuss theoretical properties of the method when the number of

parameters diverges with the sample size, and via simulations show that the

method outperforms competing methods. If time permits, I will illustrate

the method with an application to microbiome data. This work is based on

the PhD thesis of my former student Annaliza McGillivray co-supervised by

David Stephens.

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