2020
DOI: 10.5705/ss.202017.0491
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Estimation of Sparse Functional Additive Models with Adaptive Group LASSO

Abstract: Abstract:We study a flexible model to tackle the issue of lack of fit in the con-

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Cited by 8 publications
(9 citation statements)
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“…They demonstrate that this costs very little in terms of predictive ability. More recently, [72] also describe a somewhat similar way of using a LASSO-like penalty for functional regression. However, merely simplifying the shape of θ 1 ( t ) does not resolve the problem of distinguishing joint from marginal relationships.…”
Section: Approaches Towards Improving Interpretabilitymentioning
confidence: 99%
“…They demonstrate that this costs very little in terms of predictive ability. More recently, [72] also describe a somewhat similar way of using a LASSO-like penalty for functional regression. However, merely simplifying the shape of θ 1 ( t ) does not resolve the problem of distinguishing joint from marginal relationships.…”
Section: Approaches Towards Improving Interpretabilitymentioning
confidence: 99%
“…In particular, it applies a weighted penalty that is inversely proportional to the absolute value of an initial estimate of the parameter. The main goal is to favor predictors with previously known importance to avoid spurious selection (Zou 2006; Huang et al 2008). We use as initial estimates the parameters yielded by the log-linear saturated model, so that parameters with large coefficients are mildly penalized, while parameters with small coefficients are more heavily penalized.…”
Section: Empirical Evaluationmentioning
confidence: 99%
“…Other variable selection study for functional regression can be found in the sequence of monographs by Zhou et al (2013), Huang et al (2016) and Ma et al (2019). Sang et al (2020) estimated a sparse functional additive model with the adaptive group LASSO approach. It is important to note that all these investigations to functional data are for scalar-on-function regression in which the response is scalar.…”
Section: Introductionmentioning
confidence: 99%