A0410
Title: Robust estimation and variable selection in joint location and scale model using least favorable distributions
Authors: Yesim Guney - Ankara University (Turkey) [presenting]
Yetkin Tuac - Ankara University (Turkey)
Senay Ozdemir - Afyon Kocatepe University (Turkey)
Olcay Arslan - Ankara University (Turkey)
Abstract: The assumption of equal variances is not always appropriate and different approaches to modelling variance heterogeneity have been widely studied in the literature. One of these approaches is joint location and scale model (JLSM) defined with the idea that both the location and the scale depend on explanatory variables through parametric linear models. Because JLSM includes two models in itself, it does not deal well with many irrelevant variables. Therefore, determining the variables affecting the location and the scale is as important as estimating the parameters of this model. From this point of view, a combine robust estimation and variable selection method is proposed to simultaneously estimate the parameters and select the important variables. This is done using the least favorable distribution and LASSO method.
