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• W2938473691 abstract "In linear models it is common to have situations where several regression coefficients are zero. In these situations a common tool to perform regression is a variable selection operator. One of the most common such operators is the LASSO operator, which promotes point estimates which are zero. The LASSO operator and similar approaches, however, give little in terms of easily interpretable parameters to determine the degree of variable selectivity. In this paper we propose a new family of selection operators which builds on the geometry of LASSO but which yield an easily interpretable way to tune selectivity. These operators correspond to Bayesian prior densities and hence are suitable for Bayesian inference. We present some examples using simulated and real data, with promising results." @default.
• W2938473691 created "2019-04-25" @default.
• W2938473691 creator A5008281985 @default.
• W2938473691 creator A5053604005 @default.
• W2938473691 date "2019-04-11" @default.
• W2938473691 modified "2023-09-26" @default.
• W2938473691 title "FATSO: A family of operators for variable selection in linear models" @default.
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• W2938473691 hasPublicationYear "2019" @default.
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