Speaker
Description
In many empirical domains, the availability of ultrahigh-dimensional data has led to the development of feature screening and variable selection procedures aiming to detect the informative variables of datasets and consequently remove unimportant features.
In this context, we propose a ranking-based variable selection procedure that extends the Ranking Based Variable Selection technique (Baranowski et al., 2020) to general linear regression models.
We explore the performance of our proposal using both simulated and empirical data. The algorithm is compared to two competitors: i) the Extended BIC (Chen and Chen, 2012); ii) the variable selection procedure based on the combination of the Sure Independence Screening (Fan and Song, 2010) and the Elastic Net (Zou and Hastie, 2005).
References
Baranowski R, Chen Y, Fryzlewicz P (2020), Ranking-based variable selection for high-dimensional data, Statistica Sinica, 30(3), 1485-1516.
Chen J, Chen Z (2012), Extended BIC for small-n-large-P sparse GLM. Statistica Sinica, 22(2), 555-574.
Fan J, Song R (2010), Sure independence screening in generalized linear models with NP-dimensionality. The Annals of Statistics, 38(6), 3567-3604.
Zou H; Hastie T (2005), Regularization and Variable Selection via the Elastic Net. Journal of the Royal Statistical Society, Series B, 67(2), 301-320.
