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Introduction to the LASSO: A Convex Optimization Approach for High-dimensional Problems
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Indian Stat Inst, Bangalore, Karnataka, India.
2018 (English)In: Resonance, ISSN 0971-8044, Vol. 23, no 4, p. 439-464Article in journal (Refereed) Published
Abstract [en]

The term ‘high-dimensional’ refers to the case where the number of unknown parameters to be estimated, p, is of much larger order than the number of observations, n, that is pn. Since traditional statistical methods assume many observations and a few unknown variables, they can not cope up with the situations when pn. In this article, we study a statistical method, called the ‘Least Absolute Shrinkage and Selection Operator’ (LASSO), that has got much attention in solving high-dimensional problems. In particular, we consider the LASSO for high-dimensional linear regression models. We aim to provide an introduction of the LASSO method as a constrained quadratic programming problem, and we discuss the convex optimization based approach to solve the LASSO problem. We also illustrate applications of LASSO method using a simulated and a real data examples.

Place, publisher, year, edition, pages
INDIAN ACAD SCIENCES , 2018. Vol. 23, no 4, p. 439-464
Keywords [en]
LASSO, high-dimensional statistics, regularized regression, least squares regression, variable selection
National Category
Bioinformatics (Computational Biology)
Identifiers
URN: urn:nbn:se:uu:diva-357327DOI: 10.1007/s12045-018-0635-xISI: 000430459600005OAI: oai:DiVA.org:uu-357327DiVA, id: diva2:1239441
Available from: 2018-08-16 Created: 2018-08-16 Last updated: 2018-08-16Bibliographically approved

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Gauraha, Niharika
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