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Table 2 Bivariate case: required weight w for unbiased convex bootstrap estimation

From: Unbiased bootstrap error estimation for linear discriminant analysis

  n=10 n=20 n=30 n=40 n=50 n=60 n=70 n=80 n=90 n=100
ε=0.025 0.664 0.667 0.679 0.685 0.690 0.693 0.695 0.697 0.698 0.699
ε=0.050 0.666 0.637 0.638 0.639 0.641 0.642 0.642 0.643 0.644 0.644
ε=0.075 0.670 0.617 0.610 0.608 0.606 0.606 0.605 0.605 0.605 0.605
ε=0.100 0.675 0.604 0.590 0.584 0.581 0.578 0.577 0.576 0.575 0.574
ε=0.125 0.682 0.594 0.573 0.564 0.559 0.555 0.553 0.551 0.550 0.548
ε=0.150 0.691 0.588 0.560 0.547 0.539 0.534 0.530 0.528 0.526 0.524
ε=0.175 0.699 0.586 0.554 0.539 0.530 0.524 0.520 0.517 0.515 0.513
ε=0.200 0.718 0.586 0.544 0.524 0.512 0.504 0.498 0.493 0.490 0.487
ε=0.225 0.738 0.592 0.542 0.517 0.502 0.492 0.485 0.479 0.475 0.471
ε=0.250 0.759 0.603 0.545 0.515 0.497 0.485 0.476 0.469 0.464 0.460
ε=0.275 0.784 0.620 0.553 0.518 0.497 0.482 0.471 0.463 0.457 0.452
ε=0.300 0.815 0.647 0.572 0.530 0.503 0.485 0.472 0.462 0.454 0.448
ε=0.325 0.847 0.681 0.598 0.550 0.518 0.496 0.480 0.468 0.458 0.450
ε=0.350 0.882 0.728 0.639 0.584 0.546 0.520 0.500 0.484 0.472 0.462
ε=0.375 0.915 0.784 0.695 0.635 0.592 0.560 0.535 0.516 0.500 0.487
ε=0.400 0.943 0.842 0.763 0.702 0.655 0.619 0.590 0.566 0.546 0.530
ε=0.425 0.971 0.914 0.859 0.811 0.769 0.732 0.701 0.673 0.650 0.629
ε=0.450 0.987 0.960 0.933 0.905 0.879 0.853 0.830 0.807 0.786 0.766
  n =110 n =120 n =130 n =140 n =150 n =160 n =170 n =180 n =190 n =200
ε=0.025 0.700 0.701 0.701 0.702 0.702 0.703 0.703 0.704 0.704 0.704
ε=0.050 0.644 0.645 0.645 0.645 0.645 0.645 0.645 0.646 0.646 0.646
ε=0.075 0.604 0.604 0.604 0.604 0.604 0.604 0.604 0.604 0.604 0.604
ε=0.100 0.574 0.573 0.573 0.573 0.573 0.572 0.572 0.572 0.572 0.572
ε=0.125 0.548 0.547 0.546 0.546 0.545 0.545 0.544 0.544 0.544 0.543
ε=0.150 0.523 0.522 0.521 0.520 0.519 0.518 0.518 0.517 0.517 0.517
ε=0.175 0.511 0.510 0.509 0.508 0.507 0.506 0.506 0.505 0.505 0.504
ε=0.200 0.485 0.483 0.482 0.480 0.479 0.478 0.477 0.477 0.476 0.475
ε=0.225 0.469 0.466 0.464 0.463 0.461 0.460 0.459 0.458 0.457 0.456
ε=0.250 0.457 0.454 0.452 0.449 0.448 0.446 0.445 0.443 0.442 0.441
ε=0.275 0.448 0.444 0.442 0.439 0.437 0.435 0.433 0.432 0.430 0.429
ε=0.300 0.443 0.438 0.435 0.432 0.429 0.426 0.424 0.422 0.420 0.419
ε=0.325 0.444 0.439 0.434 0.430 0.426 0.423 0.421 0.418 0.416 0.414
ε=0.350 0.454 0.447 0.441 0.435 0.431 0.427 0.423 0.420 0.417 0.415
ε=0.375 0.476 0.467 0.459 0.452 0.446 0.441 0.436 0.432 0.428 0.424
ε=0.400 0.516 0.504 0.493 0.484 0.476 0.469 0.462 0.457 0.451 0.447
ε=0.425 0.611 0.594 0.580 0.567 0.555 0.544 0.535 0.526 0.518 0.511
ε=0.450 0.748 0.731 0.715 0.700 0.687 0.674 0.662 0.650 0.640 0.630