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http://dx.doi.org/10.5351/KJAS.2018.31.2.253

Analysis of internet addiction in Korean adolescents using sparse partial least-squares regression  

Han, Jeongseop (Department of mathematics, Korea Military Academy)
Park, Soobin (Department of Statistics, Ewha Womans University)
Lee, onghwan (Department of Statistics, Ewha Womans University)
Publication Information
The Korean Journal of Applied Statistics / v.31, no.2, 2018 , pp. 253-263 More about this Journal
Abstract
Internet addiction in adolescents is an important social issue. In this study, sparse partial least-squares regression (SPLS) was applied to internet addiction data in Korean adolescent samples. The internet addiction score and various clinical and psychopathological features were collected and analyzed from self-reported questionnaires. We considered three PLS methods and compared the performance in terms of prediction and sparsity. We found that the SPLS method with the hierarchical likelihood penalty was the best; in addition, two aggression features, AQ and BSAS, are important to discriminate and explain latent features of the SPLS model.
Keywords
partial least squares; dimension reduction; sparsity; random effects; hierarchical likelihood;
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