Robustness under noisy data
M = 100, N = 1000, NOISE_STD = 1.0
True OE parameters
| parameter |
value |
| $b_1$ |
1.00000000 |
| $f_1$ |
-2.40000000 |
| $f_2$ |
1.91000000 |
| $f_3$ |
-0.50400000 |
OE-structure estimates (SM, OE, and Sippy)
| method |
parameter |
mean |
std |
| SM |
$b_1$ |
1.00050556 |
0.00297019 |
| SM |
$f_1$ |
-2.39965905 |
0.00177515 |
| SM |
$f_2$ |
1.90937438 |
0.00329268 |
| SM |
$f_3$ |
-0.50371130 |
0.00153745 |
| OE |
$b_1$ |
1.00046374 |
0.00297773 |
| OE |
$f_1$ |
-2.39968286 |
0.00177458 |
| OE |
$f_2$ |
1.90941797 |
0.00329087 |
| OE |
$f_3$ |
-0.50373130 |
0.00153630 |
| Sippy |
$b_1$ |
1.04393391 |
0.05858222 |
| Sippy |
$f_1$ |
-2.37310170 |
0.03420298 |
| Sippy |
$f_2$ |
1.86040523 |
0.06301047 |
| Sippy |
$f_3$ |
-0.48102405 |
0.02916139 |
Simulation errors
| method |
mean error |
std error |
success rate (< 5%) |
| SM |
1.046883e-03 |
3.883970e-04 |
100.0\% |
| OE |
1.046365e-03 |
3.891422e-04 |
100.0\% |
| Sippy |
9.714697e-03 |
8.968038e-03 |
100.0\% |