Table 2.
Estimation error and relative prediction error, multiplied by 100, obtained from 100 Monte Carlo repetitions (with standard errors in parentheses) for dense functional data
| Metric | Model | FCS | IRLD | FSIR5 | FSIR10 | FIND |
|---|---|---|---|---|---|---|
| Estimation error |
I | 39·2 (1·6) | 45·5 (1·5) | 59·4 (2·1) | 61·7 (2·2) | 47·1 (1·6) |
| II | 35·5 (1·4) | 38·1 (1·3) | 56·1 (1·8) | 57·8 (1·9) | 44·5 (1·5) | |
| III | 59·6 (0·8) | 63·1 (0·8) | 72·6 (1·1) | 74·1 (1·3) | 63·6 (0·9) | |
| IV | 57·2 (0·6) | 59·0 (0·6) | 69·3 (1·0) | 68·9 (0·9) | 61·0 (0·8) | |
| Prediction error |
I | 11·1 (0·6) | 12·7 (0·5) | 17·1 (0·7) | 16·7 (0·6) | 16·1 (1·1) |
| II | 9·8 (0·5) | 10·5 (0·4) | 15·5 (0·7) | 16·9 (1·0) | 14·9 (0·8) | |
| III | 13·5 (0·5) | 15·2 (0·5) | 15·8 (0·6) | 16·6 (0·5) | 14·7 (0·6) | |
| IV | 19·9 (0·7) | 21·9 (0·7) | 31·1 (1·4) | 32·2 (1·4) | 24·2 (1·2) |
FCS, functional cumulative slicing; IRLD, inverse regression for longitudinal data (Jiang et al., 2014); FSIR5, functional sliced inverse regression (Ferré & Yao, 2003) with five slices; FSIR10, functional sliced inverse regression (Ferré & Yao, 2003) with ten slices; FIND, functional index model (Chen et al., 2011).