Name
Abstract | ||
|---|---|---|
The problem of dimension reduction in multiple regressions is investigated in this paper, in which data are from several populations that share the same variables. Assuming that the set of relevant predictors is the same across the regressions, a joint estimation and selection method is proposed, aiming to preserve the common structure, while allowing for population-specific characteristics. The new approach is based upon the relationship between sliced inverse regression and multiple linear regression, and is achieved through the lasso shrinkage penalty. A fast alternating algorithm is developed to solve the corresponding optimization problem. The performance of the proposed method is illustrated through simulated and real data examples. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/s11222-015-9609-y | Statistics and Computing |
Keywords | Field | DocType |
Joint sparsity,Multiple regressions,Sliced inverse regression,Sufficient dimension reduction | Population,Mathematical optimization,Dimensionality reduction,Shrinkage,Sliced inverse regression,Lasso (statistics),Statistics,Sufficient dimension reduction,Optimization problem,Mathematics,Linear regression | Journal |
Volume | Issue | ISSN |
27 | 1 | 0960-3174 |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| tao wang | 1 | 0 | 1.01 |
| Xuerong Meggie Wen | 2 | 2 | 1.23 |
| Lixing Zhu | 3 | 116 | 34.41 |