Sure independence screening for real medical Poisson data. Issue 2 (25th January 2019)

• Record Type: Journal Article
• Authors: Drosou, K.; Koukouvinos, C.; Lappa, A.
• Is Part Of: Journal of applied statistics. Volume 46:Issue 2(2019)
• Page Start: 324
• Page End: 350
• Publication Date: 2019-01-25
• Subjects: Generalized linear models -- medical data -- penalized likelihood; 62P10 -- 62J12 -- 62-07; Statistics -- Periodicals; 519.5
• DOI: 10.1080/02664763.2018.1480708
• Languages: English
• ISSNs: 0266-4763

Abstract

ABSTRACT: The statistical modeling of big data bases constitutes one of the most challenging issues, especially nowadays. The issue is even more critical in case of a complicated correlation structure. Variable selection plays a vital role in statistical analysis of large data bases and many methods have been proposed so far to deal with the aforementioned problem. One of such methods is the Sure Independence Screening which has been introduced to reduce dimensionality to a relatively smaller scale. This method, though simple, produces remarkable results even under both ultra high dimensionality and big scale in terms of sample size problems. In this paper we dealt with the analysis of a big real medical data set assuming a Poisson regression model. We support the analysis by conducting simulated experiments taking into consideration the correlation structure of the design matrix.