### Statistical inference for high-dimensional differential networks

Statistics Seminar

23rd October 2020, 4:00 pm – 5:00 pm

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Abstract:

We present a recent line of work on estimating differential networks

and conducting statistical inference about parameters in a

high-dimensional setting. First, we consider a Gaussian setting and

show how to directly learn the difference between the graph

structures. A debiasing procedure will be presented for construction

of an asymptotically normal estimator of the difference. Next,

building on the first part, we show how to learn the difference

between two graphical models with latent variables. Linear convergence

rate is established for an alternating gradient descent procedure with

correct initialization. Simulation studies illustrate performance of

the procedure. Finally, we will discuss how to do statistical

inference on the differential networks when data are not Gaussian.

*Organiser*: Juliette Unwin

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