Name
Abstract | ||
|---|---|---|
Existing estimators of the central mean space are known to have uneven performances across different types of link functions. By combining the strength of the ordinary least squares and the principal Hessian directions, the authors propose a new hybrid estimator that successfully recovers the central mean space for a wide range of link functions. Based on the new hybrid estimator, the authors further study the order determination procedure and the marginal coordinate test. The superior performance of the hybrid estimator over existing methods is demonstrated in extensive simulation studies. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/s11424-017-6227-0 | J. Systems Science & Complexity |
Keywords | Field | DocType |
Marginal coordinate test, order determination, ordinary least squares, principal Hessian directions, sufficient dimension reduction | Minimum-variance unbiased estimator,Mathematical optimization,Ordinary least squares,Hessian matrix,Sufficient dimension reduction,Mathematics,Estimator | Journal |
Volume | Issue | ISSN |
30 | 1 | 1559-7067 |
Citations | PageRank | References |
1 | 0.37 | 2 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qi Xia | 1 | 132 | 21.76 |
| Yuexiao Dong | 2 | 3 | 4.67 |