A nonlinear mixed-integer programming approach for variable selection in linear regression model

Modern statistical studies often encounter regression models with high dimensions in which the number of features p is greater than the sample size n. Although the theory of linear models is well-established for the traditional assumption p < n, making valid statistical inference in high dimensio...

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Vydáno v:	Communications in statistics. Simulation and computation Ročník 52; číslo 11; s. 5434 - 5445
Hlavní autoři:	Roozbeh, Mahdi, Babaie-Kafaki, Saman, Aminifard, Zohre
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Philadelphia Taylor & Francis 02.11.2023 Taylor & Francis Ltd
Témata:	Integer programming LASSO method Linear regression model Mixed-integer programming Regression models Riboflavin Sparsity Statistical analysis Statistical inference Variable selection
ISSN:	0361-0918, 1532-4141
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