A comparative study of the Lasso-type and heuristic model selection methods
No 42, Working Papers from COMISEF
This study presents a first comparative analysis of Lasso-type (Lasso, adaptive Lasso, elastic net) and heuristic subset selection methods. Although the Lasso has shown success in many situations, it has some limitations. In particular, inconsistent results are obtained for pairwise strongly correlated predictors. An alternative to the Lasso is constituted by model selection based on information criteria (IC), which remains consistent in the situation mentioned. However, these criteria are hard to optimize due to a discrete search space. To overcome this problem, an optimization heuristic (Genetic Algorithm) is applied. Monte-Carlo simulation results are reported to illustrate the performance of the methods.
Keywords: Model selection; Lasso; adaptive Lasso; elastic net; heuristic methods; genetic algorithms (search for similar items in EconPapers)
Pages: 28 pages
New Economics Papers: this item is included in nep-cmp, nep-ecm and nep-ore
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Journal Article: A Comparative Study of the Lasso-type and Heuristic Model Selection Methods (2013)
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Persistent link: https://EconPapers.repec.org/RePEc:com:wpaper:042
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