抄録
We consider the problem of constructing a reduced-rank regression model whose coefficient parameter is represented as a singular value decomposition with sparse singular vectors. The traditional estimation procedure for the coefficient parameter often fails when the true rank of the parameter is high. To overcome this issue, we develop an estimation algorithm with rank and variable selection via sparse regularization and manifold optimization, which enables us to obtain an accurate estimation of the coefficient parameter even if the true rank of the coefficient parameter is high. Using sparse regularization, we can also select an optimal value of the rank. We conduct Monte Carlo experiments and a real data analysis to illustrate the effectiveness of our proposed method.
本文言語 | 英語 |
---|---|
ページ(範囲) | 53-75 |
ページ数 | 23 |
ジャーナル | Computational Statistics |
巻 | 38 |
号 | 1 |
DOI | |
出版ステータス | 出版済み - 3月 2023 |
外部発表 | はい |
!!!All Science Journal Classification (ASJC) codes
- 統計学および確率
- 統計学、確率および不確実性
- 計算数学