Name
Abstract | ||
|---|---|---|
Principal component regression (PCR) is a two-stage procedure that selects some principal components and then constructs a regression model regarding them as new explanatory variables. Note that the principal components are obtained from only explanatory variables and not considered with the response variable. To address this problem, we propose the sparse principal component regression (SPCR) that is a one-stage procedure for PCR. SPCR enables us to adaptively obtain sparse principal component loadings that are related to the response variable and select the number of principal components simultaneously. SPCR can be obtained by the convex optimization problem for each parameter with the coordinate descent algorithm. Monte Carlo simulations and real data analyses are performed to illustrate the effectiveness of SPCR. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1016/j.csda.2015.03.016 | Computational Statistics & Data Analysis |
Keywords | Field | DocType |
dimension reduction,regularization,sparsity,principal component regression,identifiability | Econometrics,Monte Carlo method,Dimensionality reduction,Principal component regression,Regression analysis,Identifiability,Coordinate descent,Statistics,Convex optimization,Mathematics,Principal component analysis | Journal |
Volume | Issue | ISSN |
89 | C | Computational Statistics & Data Analysis 89 (2015) 192-203 |
Citations | PageRank | References |
5 | 0.62 | 6 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shuichi Kawano | 1 | 13 | 3.72 |
| Hironori Fujisawa | 2 | 61 | 4.74 |
| Toyoyuki Takada | 3 | 23 | 1.44 |
| Toshihiko Shiroishi | 4 | 32 | 4.33 |