Λέσχη Φίλων Στατιστικής - GrStats forum
AUEB STATS SEMINARS 20/12/2019:  Latent group structure and regularized regression by Konstantinos Perrakis Forumgrstats

Join the forum, it's quick and easy

Λέσχη Φίλων Στατιστικής - GrStats forum
AUEB STATS SEMINARS 20/12/2019:  Latent group structure and regularized regression by Konstantinos Perrakis Forumgrstats
Λέσχη Φίλων Στατιστικής - GrStats forum
Would you like to react to this message? Create an account in a few clicks or log in to continue.
Για προβλήματα εγγραφής και άλλες πληροφορίες επικοινωνήστε με : grstats.forum@gmail.com ή grstats@stat-athens.aueb.gr

Go down
grstats
grstats
Posts : 860
Join date : 2009-10-21
http://stat-athens.aueb.gr/~grstats/

AUEB STATS SEMINARS 20/12/2019:  Latent group structure and regularized regression by Konstantinos Perrakis Empty AUEB STATS SEMINARS 20/12/2019: Latent group structure and regularized regression by Konstantinos Perrakis

Wed 18 Dec 2019 - 9:17
AUEB STATISTICS SEMINAR SERIES
DECEMBER 2019

Konstantinos Perrakis
Department of Mathematical Sciences, Durham University

Latent group structure and regularized regression

FRIDAY 20/11/2019, 13:15

Room T103,
Troias 2,  New AUEB Building

ABSTRACT

Regression modelling typically assumes homogeneity of the conditional distribution of responses Y given features X. For inhomogeneous data, with latent groups having potentially different underlying distributions, the hidden group structure can be crucial for estimation and prediction, and standard regression models may be  severely confounded. Worse, in the multivariate setting, the presence of such inhomogeneity can easily pass undetected. To allow for robust and interpretable regression modelling in the heterogeneous data setting we put forward a class of mixture models that couples together both the multivariate marginal on X and the conditional Y | X to capture the latent group structure. This joint modelling approach allows for group-specific regression parameters, automatically controlling for the latent confounding that may otherwise pose difficulties, and offers a novel way to deal with suspected distributional shifts in the data. We show how the latent variable model can be regularized to provide scalable solutions with explicit sparsity. Estimation is handled via an expectation-maximization algorithm. We illustrate the key ideas via empirical examples.

Facebook event page: https://www.facebook.com/events/482391029056553/
Back to top
Permissions in this forum:
You cannot reply to topics in this forum