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Sure independence screening for real medical Poisson data

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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.
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Keywords: 62-07; 62J12; 62P10; Generalized linear models; medical data; penalized likelihood

Document Type: Research Article

Affiliations: Department of Mathematics, National Technical University of Athens, Zografou, Greece

Publication date: January 25, 2019

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