Introduction to the LASSO
| Autor: | Niharika Gauraha |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Mathematical optimization
Computer science 02 engineering and technology High dimensional 021001 nanoscience & nanotechnology 01 natural sciences Statistics::Computation Education Term (time) 010104 statistics & probability Lasso (statistics) Linear regression Convex optimization Statistics::Methodology Order (group theory) Quadratic programming 0101 mathematics 0210 nano-technology Selection operator |
| Zdroj: | Resonance. 23:439-464 |
| ISSN: | 0973-712X 0971-8044 |
| Popis: | 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 p ≫ n. Since traditional statistical methods assume many observations and a few unknown variables, they can not cope up with the situations when p ≫ n. 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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