[Forum SIS] PHD Seminars @Sapienza

[Marco Alfo']

We are glad to announce the following Seminar

monday, december 16, 14.00 pm

 Room VII (Castellano), Statistical Sciences Building CU002, Sapienza
Universita' di Roma
Roberto Di Mari (Università di Catania)
Lasso-penalized clusterwise linear regression

Abstract
In clusterwise regression analysis, the goal is to predict a response
variable based on a set of explanatory variables, where each predictor has
different contributions to the response depending on the cluster. The
number of candidates is typically large: whereas some of these variables
might be useful, some others might contribute very little to the
prediction. A well known method to perform variable selection is the lasso,
where the penalty is calibrated by minimizing the Bayesian Information
Criterion (BIC). However, available approaches to the computation of
lasso-penalized estimators are time consuming and/or require approximate
schemes making the tuning of the penalty cumbersome. In order to ease such
computation, we introduce an expectation maximization algorithm with
closed-form updates. This is based on an iterative scheme where the Least
Angle Regression algorithm is used to update the component specific
regression coefficients. We show that this approach, in addition to
shortening the calculation times of the lasso-penalized solution, gives an
optimal grid for BIC minimization. The method is assessed by means of a
simulation study and an application to Major League Baseball salary data
from the 1990s.

Key words: clusterwise linear regression, penalized likelihood, feature
selection.


Joint work with
Roberto Rocci
Università di Roma La Sapienza
Stefano Antonio Gattone
Università di Chieti-Pescara G. d'Annunzio
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://www.stat.unipg.it/pipermail/sis/attachments/20191212/1c9074a7/attachment.html>