Shrinkage estimation and selection for multiple functional regression
By Heng Lian. Statistica sinica, 2011
59 citations recorded by the source corpus.
Functional linear regression is a useful extension of simple linear re- gression and has been investigated by many researchers. However, the functional variable selection problem when multiple functional observations exist, which is the counterpart in the functional context of multiple linear regression, is seldom stud- ied. Here we propose a method using a group smoothly clipped absolute deviation penalty (gSCAD) which can perform regression estimation and variable selection simultaneously. We show the method can identify the true model consistently, and discuss construction of pointwise confidence intervals for the estimated functional coefficients. Our methodology and theory is verified by simulation studies as well as some applications to data.
Subfields: Control and Systems Engineering, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/1819f7a75df4b45b35ed1ab8a4f3e3677c8af245
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In Control and Systems Engineering
In Statistics and Probability
Citations in the corpus
- A statistical method for estimating predictable differences between daily traffic flow profiles (2017, methodology): “Techniques for simultaneous variable selection and estimation, such as LASSO (Tibshirani, 1996), have been applied in functional analysis contexts (Matsui and Konishi, 2011, Lian, 2013, Mingotti et al., 2013).”