Name
Abstract | ||
|---|---|---|
We propose a semiparametric framework based on sliced inverse regression (SIR) to address the issue of variable selection in functional regression. SIR is an effective method for dimension reduction which computes a linear projection of the predictors in a low-dimensional space, without loss of information on the regression. In order to deal with the high dimensionality of the predictors, we consider penalized versions of SIR: ridge and sparse. We extend the approaches of variable selection developed for multidimensional SIR to select intervals that form a partition of the definition domain of the functional predictors. Selecting entire intervals rather than separated evaluation points improves the interpretability of the estimated coefficients in the functional framework. A fully automated iterative procedure is proposed to find the critical (interpretable) intervals. The approach is proved efficient on simulated and real data. The method is implemented in the R package SISIR available on CRAN at https://cran.r-project.org/package=SISIR. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/s11222-018-9806-6 | Statistics and Computing |
Keywords | Field | DocType |
Functional regression, SIR, Lasso, Ridge regression, Interval selection | Econometrics,Interpretability,Dimensionality reduction,Feature selection,Regression,Effective method,Sliced inverse regression,Functional regression,Projection (linear algebra),Statistics,Mathematics | Journal |
Volume | Issue | ISSN |
29 | 2 | 1573-1375 |
Citations | PageRank | References |
1 | 0.38 | 10 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Victor Picheny | 1 | 131 | 14.82 |
| Rémi Servien | 2 | 1 | 1.06 |
| Nathalie Villa-Vialaneix | 3 | 72 | 10.94 |