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class="gsc_oci_title_ggi"><a href="https://projecteuclid.org/journals/annals-of-statistics/volume-24/issue-6/Heuristics-of-instability-and-stabilization-in-model-selection/10.1214/aos/1032181158.pdf" data-clk="hl=en&sa=T&ei=J4JHZ6iZCZuDy9YPs-PngA4"><span class='gsc_vcd_title_ggt'>[PDF]</span> from projecteuclid.org</a></div></div><div id="gsc_oci_title"><a class="gsc_oci_title_link" href="https://projecteuclid.org/journals/annals-of-statistics/volume-24/issue-6/Heuristics-of-instability-and-stabilization-in-model-selection/10.1214/aos/1032181158.short" data-clk="hl=en&sa=T&ei=J4JHZ6iZCZuDy9YPs-PngA4">Heuristics of instability and stabilization in model selection</a></div></div><div id="gsc_oci_table"><div class="gs_scl"><div class="gsc_oci_field">Authors</div><div class="gsc_oci_value">Leo Breiman</div></div><div class="gs_scl"><div class="gsc_oci_field">Publication date</div><div class="gsc_oci_value">1996</div></div><div class="gs_scl"><div class="gsc_oci_field">Journal</div><div class="gsc_oci_value">The Annals of Statistics</div></div><div class="gs_scl"><div class="gsc_oci_field">Volume</div><div class="gsc_oci_value">24</div></div><div class="gs_scl"><div class="gsc_oci_field">Issue</div><div class="gsc_oci_value">6</div></div><div class="gs_scl"><div class="gsc_oci_field">Pages</div><div class="gsc_oci_value">2350-2383</div></div><div class="gs_scl"><div class="gsc_oci_field">Publisher</div><div class="gsc_oci_value">Institute of Mathematical Statistics</div></div><div class="gs_scl"><div class="gsc_oci_field">Description</div><div class="gsc_oci_value" id="gsc_oci_descr"><div class="gsh_small">In model selection, usually a "best" predictor is chosen from a collection <svg class="gs_fsvg" aria-label="{\hat{\mu}(\cdot, s)}" width="36px" height="14px" style="vertical-align:-4px;"><g transform="matrix(0.01400, 0.00000, 0.00000, 0.01400, 0.00000, 10.50000)"><g><g><g><path transform="scale(0.48828, -0.48828)" d="M 57 -385 Q 57 -373 59 -369 L 360 840 Q 369 869 392 887 T 446 905 Q 472 905 490 889 T 508 848 Q 508 842 507 838 T 506 831 L 385 352 Q 365 267 365 205 Q 365 129 401 80 T 510 31 Q 659 31 772 217 Q 773 219 773 220 T 774 223 L 922 817 Q 929 844 954 863 T 1008 883 Q 1032 883 1050 867 T 1069 825 Q 1069 813 1067 809 L 920 219 Q 905 161 905 119 Q 905 31 965 31 Q 1029 31 1061 110 T 1118 301 Q 1122 313 1135 313 H 1159 Q 1167 313 1172 306 T 1178 293 Q 1142 150 1099 63 T 961 -23 Q 893 -23 840 16 T 772 121 Q 721 57 652 17 T 508 -23 Q 380 -23 307 37 L 203 -377 Q 197 -406 173 -424 T 119 -442 Q 94 -442 75 -427 T 57 -385 Z "/></g><g transform="translate(79.05900, -11.44501)"><path transform="scale(0.48828, -0.48828)" d="M 276 1102 L 236 1145 L 512 1421 L 786 1145 L 745 1102 L 512 1307 L 276 1102 Z "/></g></g><path transform="matrix(0.48828, 0.00000, 0.00000, -0.48828, 602.56000, 0.00000)" d="M 635 -508 Q 521 -418 438 -302 T 303 -53 T 225 223 T 199 512 Q 199 659 225 803 T 304 1080 T 441 1329 T 635 1532 Q 635 1536 645 1536 H 664 Q 670 1536 675 1530 T 680 1518 Q 680 1509 676 1505 Q 576 1407 509 1295 T 402 1056 T 344 794 T 326 512 Q 326 -139 674 -477 Q 680 -483 680 -494 Q 680 -499 674 -506 T 664 -512 H 645 Q 635 -512 635 -508 Z "/><path transform="matrix(0.48828, 0.00000, 0.00000, -0.48828, 991.45001, 0.00000)" d="M 172 512 Q 172 541 187 567 T 228 609 T 285 625 Q 314 625 340 609 T 382 567 T 397 512 Q 397 466 364 432 T 285 399 Q 239 399 205 432 T 172 512 Z "/><path transform="matrix(0.48828, 0.00000, 0.00000, -0.48828, 1269.22998, 0.00000)" d="M 203 -369 Q 203 -360 211 -352 Q 285 -281 326 -188 T 367 8 V 33 Q 334 0 285 0 Q 238 0 205 33 T 172 113 Q 172 161 205 193 T 285 225 Q 358 225 389 157 T 420 8 Q 420 -106 374 -209 T 246 -393 Q 238 -397 233 -397 Q 223 -397 213 -388 T 203 -369 Z "/><path transform="matrix(0.48828, 0.00000, 0.00000, -0.48828, 1713.67712, 0.00000)" d="M 178 125 Q 233 31 399 31 Q 471 31 536 55 T 643 129 T 686 248 Q 686 301 648 335 T 555 381 L 444 403 Q 368 422 319 474 T 270 600 Q 270 691 319 761 T 450 868 T 618 905 Q 711 905 784 860 T 858 729 Q 858 682 831 646 T 758 610 Q 731 610 711 627 T 692 672 Q 692 696 705 718 T 741 754 T 788 768 Q 770 812 720 832 T 614 852 Q 562 852 510 831 T 426 769 T 395 674 Q 395 637 421 609 T 485 569 L 604 545 Q 661 533 708 502 T 783 425 T 811 319 Q 811 243 768 169 T 664 51 Q 555 -23 397 -23 Q 288 -23 197 27 T 106 176 Q 106 232 138 273 T 227 315 Q 260 315 282 295 T 305 242 Q 305 195 270 160 T 188 125 H 178 Z "/><path transform="matrix(0.48828, 0.00000, 0.00000, -0.48828, 2182.45703, 0.00000)" d="M 133 -512 Q 115 -512 115 -494 Q 115 -485 119 -481 Q 469 -139 469 512 Q 469 1163 123 1501 Q 115 1506 115 1518 Q 115 1525 120 1530 T 133 1536 H 152 Q 158 1536 162 1532 Q 309 1416 407 1250 T 550 896 T 596 512 Q 596 367 571 226 T 493 -51 T 358 -303 T 162 -508 Q 158 -512 152 -512 H 133 Z "/></g></g></svg> of predictors where <svg class="gs_fsvg" aria-label="\hat{\mu}(\cdot, s)" width="36px" height="14px" style="vertical-align:-4px;"><g transform="matrix(0.01400, 0.00000, 0.00000, 0.01400, 0.00000, 10.50000)"><g><g><path transform="scale(0.48828, -0.48828)" d="M 57 -385 Q 57 -373 59 -369 L 360 840 Q 369 869 392 887 T 446 905 Q 472 905 490 889 T 508 848 Q 508 842 507 838 T 506 831 L 385 352 Q 365 267 365 205 Q 365 129 401 80 T 510 31 Q 659 31 772 217 Q 773 219 773 220 T 774 223 L 922 817 Q 929 844 954 863 T 1008 883 Q 1032 883 1050 867 T 1069 825 Q 1069 813 1067 809 L 920 219 Q 905 161 905 119 Q 905 31 965 31 Q 1029 31 1061 110 T 1118 301 Q 1122 313 1135 313 H 1159 Q 1167 313 1172 306 T 1178 293 Q 1142 150 1099 63 T 961 -23 Q 893 -23 840 16 T 772 121 Q 721 57 652 17 T 508 -23 Q 380 -23 307 37 L 203 -377 Q 197 -406 173 -424 T 119 -442 Q 94 -442 75 -427 T 57 -385 Z "/></g><g transform="translate(79.05900, -11.44501)"><path transform="scale(0.48828, -0.48828)" d="M 276 1102 L 236 1145 L 512 1421 L 786 1145 L 745 1102 L 512 1307 L 276 1102 Z "/></g></g><path transform="matrix(0.48828, 0.00000, 0.00000, -0.48828, 602.56000, 0.00000)" d="M 635 -508 Q 521 -418 438 -302 T 303 -53 T 225 223 T 199 512 Q 199 659 225 803 T 304 1080 T 441 1329 T 635 1532 Q 635 1536 645 1536 H 664 Q 670 1536 675 1530 T 680 1518 Q 680 1509 676 1505 Q 576 1407 509 1295 T 402 1056 T 344 794 T 326 512 Q 326 -139 674 -477 Q 680 -483 680 -494 Q 680 -499 674 -506 T 664 -512 H 645 Q 635 -512 635 -508 Z "/><path transform="matrix(0.48828, 0.00000, 0.00000, -0.48828, 991.45001, 0.00000)" d="M 172 512 Q 172 541 187 567 T 228 609 T 285 625 Q 314 625 340 609 T 382 567 T 397 512 Q 397 466 364 432 T 285 399 Q 239 399 205 432 T 172 512 Z "/><path transform="matrix(0.48828, 0.00000, 0.00000, -0.48828, 1269.22998, 0.00000)" d="M 203 -369 Q 203 -360 211 -352 Q 285 -281 326 -188 T 367 8 V 33 Q 334 0 285 0 Q 238 0 205 33 T 172 113 Q 172 161 205 193 T 285 225 Q 358 225 389 157 T 420 8 Q 420 -106 374 -209 T 246 -393 Q 238 -397 233 -397 Q 223 -397 213 -388 T 203 -369 Z "/><path transform="matrix(0.48828, 0.00000, 0.00000, -0.48828, 1713.67712, 0.00000)" d="M 178 125 Q 233 31 399 31 Q 471 31 536 55 T 643 129 T 686 248 Q 686 301 648 335 T 555 381 L 444 403 Q 368 422 319 474 T 270 600 Q 270 691 319 761 T 450 868 T 618 905 Q 711 905 784 860 T 858 729 Q 858 682 831 646 T 758 610 Q 731 610 711 627 T 692 672 Q 692 696 705 718 T 741 754 T 788 768 Q 770 812 720 832 T 614 852 Q 562 852 510 831 T 426 769 T 395 674 Q 395 637 421 609 T 485 569 L 604 545 Q 661 533 708 502 T 783 425 T 811 319 Q 811 243 768 169 T 664 51 Q 555 -23 397 -23 Q 288 -23 197 27 T 106 176 Q 106 232 138 273 T 227 315 Q 260 315 282 295 T 305 242 Q 305 195 270 160 T 188 125 H 178 Z "/><path transform="matrix(0.48828, 0.00000, 0.00000, -0.48828, 2182.45703, 0.00000)" d="M 133 -512 Q 115 -512 115 -494 Q 115 -485 119 -481 Q 469 -139 469 512 Q 469 1163 123 1501 Q 115 1506 115 1518 Q 115 1525 120 1530 T 133 1536 H 152 Q 158 1536 162 1532 Q 309 1416 407 1250 T 550 896 T 596 512 Q 596 367 571 226 T 493 -51 T 358 -303 T 162 -508 Q 158 -512 152 -512 H 133 Z "/></g></svg> is the minimum least-squares predictor in a collection <svg class="gs_fsvg" aria-label="\mathsf{U}_s" width="16px" height="12px" style="vertical-align:-2px;"><g transform="matrix(0.01400, 0.00000, 0.00000, 0.01400, 0.00000, 9.71600)"><g><g><path transform="scale(0.48828, -0.48828)" d="M 188 473 V 1421 H 379 V 473 Q 379 370 416 279 T 528 133 T 709 78 Q 789 78 853 109 T 961 196 T 1028 323 T 1051 473 V 1421 H 1219 V 473 Q 1219 336 1151 216 T 965 26 T 709 -45 Q 571 -45 450 24 T 258 213 T 188 473 Z "/></g><g transform="translate(687.50000, 150.00000)"><path transform="scale(0.34180, -0.34180)" d="M 244 123 Q 300 45 477 45 Q 544 45 615 65 T 734 131 T 782 248 Q 782 299 738 330 T 637 373 L 504 397 Q 425 413 370 468 T 315 598 Q 315 693 369 762 T 511 867 T 692 903 Q 752 903 815 885 T 919 828 T 961 727 Q 961 678 933 641 T 858 604 Q 829 604 807 623 T 786 672 Q 786 707 810 735 T 868 770 Q 822 838 688 838 Q 601 838 528 796 T 455 672 Q 455 597 561 573 L 696 549 Q 788 531 855 470 T 922 319 Q 922 242 876 168 T 762 51 Q 647 -20 475 -20 Q 350 -20 247 25 T 145 178 Q 145 235 179 277 T 270 319 Q 305 319 328 298 T 352 242 Q 352 196 321 162 T 244 123 Z "/></g></g></g></svg> of predictors. Here s is a complexity parameter; that is, the smaller s, the lower dimensional/smoother the models in <svg class="gs_fsvg" aria-label="\mathsf{U}_s" width="16px" height="12px" style="vertical-align:-2px;"><g transform="matrix(0.01400, 0.00000, 0.00000, 0.01400, 0.00000, 9.71600)"><g><g><path transform="scale(0.48828, -0.48828)" d="M 188 473 V 1421 H 379 V 473 Q 379 370 416 279 T 528 133 T 709 78 Q 789 78 853 109 T 961 196 T 1028 323 T 1051 473 V 1421 H 1219 V 473 Q 1219 336 1151 216 T 965 26 T 709 -45 Q 571 -45 450 24 T 258 213 T 188 473 Z "/></g><g transform="translate(687.50000, 150.00000)"><path transform="scale(0.34180, -0.34180)" d="M 244 123 Q 300 45 477 45 Q 544 45 615 65 T 734 131 T 782 248 Q 782 299 738 330 T 637 373 L 504 397 Q 425 413 370 468 T 315 598 Q 315 693 369 762 T 511 867 T 692 903 Q 752 903 815 885 T 919 828 T 961 727 Q 961 678 933 641 T 858 604 Q 829 604 807 623 T 786 672 Q 786 707 810 735 T 868 770 Q 822 838 688 838 Q 601 838 528 796 T 455 672 Q 455 597 561 573 L 696 549 Q 788 531 855 470 T 922 319 Q 922 242 876 168 T 762 51 Q 647 -20 475 -20 Q 350 -20 247 25 T 145 178 Q 145 235 179 277 T 270 319 Q 305 319 328 298 T 352 242 Q 352 196 321 162 T 244 123 Z "/></g></g></g></svg>. If <svg class="gs_fsvg" aria-label="\mathsf{L}" width="8px" height="10px" style="vertical-align:0px;"><g transform="matrix(0.01400, 0.00000, 0.00000, 0.01400, 0.00000, 9.71600)"><g><path transform="scale(0.48828, -0.48828)" d="M 188 0 V 1421 H 379 V 139 L 1022 143 V 0 H 188 Z "/></g></g></svg> is the data used to derive the sequence <svg class="gs_fsvg" aria-label="{\hat{\mu}(\cdot, s)}" width="36px" height="14px" style="vertical-align:-4px;"><g transform="matrix(0.01400, 0.00000, 0.00000, 0.01400, 0.00000, 10.50000)"><g><g><g><path transform="scale(0.48828, -0.48828)" d="M 57 -385 Q 57 -373 59 -369 L 360 840 Q 369 869 392 887 T 446 905 Q 472 905 490 889 T 508 848 Q 508 842 507 838 T 506 831 L 385 352 Q 365 267 365 205 Q 365 129 401 80 T 510 31 Q 659 31 772 217 Q 773 219 773 220 T 774 223 L 922 817 Q 929 844 954 863 T 1008 883 Q 1032 883 1050 867 T 1069 825 Q 1069 813 1067 809 L 920 219 Q 905 161 905 119 Q 905 31 965 31 Q 1029 31 1061 110 T 1118 301 Q 1122 313 1135 313 H 1159 Q 1167 313 1172 306 T 1178 293 Q 1142 150 1099 63 T 961 -23 Q 893 -23 840 16 T 772 121 Q 721 57 652 17 T 508 -23 Q 380 -23 307 37 L 203 -377 Q 197 -406 173 -424 T 119 -442 Q 94 -442 75 -427 T 57 -385 Z "/></g><g transform="translate(79.05900, -11.44501)"><path transform="scale(0.48828, -0.48828)" d="M 276 1102 L 236 1145 L 512 1421 L 786 1145 L 745 1102 L 512 1307 L 276 1102 Z "/></g></g><path transform="matrix(0.48828, 0.00000, 0.00000, -0.48828, 602.56000, 0.00000)" d="M 635 -508 Q 521 -418 438 -302 T 303 -53 T 225 223 T 199 512 Q 199 659 225 803 T 304 1080 T 441 1329 T 635 1532 Q 635 1536 645 1536 H 664 Q 670 1536 675 1530 T 680 1518 Q 680 1509 676 1505 Q 576 1407 509 1295 T 402 1056 T 344 794 T 326 512 Q 326 -139 674 -477 Q 680 -483 680 -494 Q 680 -499 674 -506 T 664 -512 H 645 Q 635 -512 635 -508 Z "/><path transform="matrix(0.48828, 0.00000, 0.00000, -0.48828, 991.45001, 0.00000)" d="M 172 512 Q 172 541 187 567 T 228 609 T 285 625 Q 314 625 340 609 T 382 567 T 397 512 Q 397 466 364 432 T 285 399 Q 239 399 205 432 T 172 512 Z "/><path transform="matrix(0.48828, 0.00000, 0.00000, -0.48828, 1269.22998, 0.00000)" d="M 203 -369 Q 203 -360 211 -352 Q 285 -281 326 -188 T 367 8 V 33 Q 334 0 285 0 Q 238 0 205 33 T 172 113 Q 172 161 205 193 T 285 225 Q 358 225 389 157 T 420 8 Q 420 -106 374 -209 T 246 -393 Q 238 -397 233 -397 Q 223 -397 213 -388 T 203 -369 Z "/><path transform="matrix(0.48828, 0.00000, 0.00000, -0.48828, 1713.67712, 0.00000)" d="M 178 125 Q 233 31 399 31 Q 471 31 536 55 T 643 129 T 686 248 Q 686 301 648 335 T 555 381 L 444 403 Q 368 422 319 474 T 270 600 Q 270 691 319 761 T 450 868 T 618 905 Q 711 905 784 860 T 858 729 Q 858 682 831 646 T 758 610 Q 731 610 711 627 T 692 672 Q 692 696 705 718 T 741 754 T 788 768 Q 770 812 720 832 T 614 852 Q 562 852 510 831 T 426 769 T 395 674 Q 395 637 421 609 T 485 569 L 604 545 Q 661 533 708 502 T 783 425 T 811 319 Q 811 243 768 169 T 664 51 Q 555 -23 397 -23 Q 288 -23 197 27 T 106 176 Q 106 232 138 273 T 227 315 Q 260 315 282 295 T 305 242 Q 305 195 270 160 T 188 125 H 178 Z "/><path transform="matrix(0.48828, 0.00000, 0.00000, -0.48828, 2182.45703, 0.00000)" d="M 133 -512 Q 115 -512 115 -494 Q 115 -485 119 -481 Q 469 -139 469 512 Q 469 1163 123 1501 Q 115 1506 115 1518 Q 115 1525 120 1530 T 133 1536 H 152 Q 158 1536 162 1532 Q 309 1416 407 1250 T 550 896 T 596 512 Q 596 367 571 226 T 493 -51 T 358 -303 T 162 -508 Q 158 -512 152 -512 H 133 Z "/></g></g></svg>, the procedure is called unstable if a small change in <svg class="gs_fsvg" aria-label="\mathsf{L}" width="8px" height="10px" style="vertical-align:0px;"><g transform="matrix(0.01400, 0.00000, 0.00000, 0.01400, 0.00000, 9.71600)"><g><path transform="scale(0.48828, -0.48828)" d="M 188 0 V 1421 H 379 V 139 L 1022 143 V 0 H 188 Z "/></g></g></svg> can cause large changes in <svg class="gs_fsvg" aria-label="{\hat{\mu}(\cdot, s)}" width="36px" height="14px" style="vertical-align:-4px;"><g transform="matrix(0.01400, 0.00000, 0.00000, 0.01400, 0.00000, 10.50000)"><g><g><g><path transform="scale(0.48828, -0.48828)" d="M 57 -385 Q 57 -373 59 -369 L 360 840 Q 369 869 392 887 T 446 905 Q 472 905 490 889 T 508 848 Q 508 842 507 838 T 506 831 L 385 352 Q 365 267 365 205 Q 365 129 401 80 T 510 31 Q 659 31 772 217 Q 773 219 773 220 T 774 223 L 922 817 Q 929 844 954 863 T 1008 883 Q 1032 883 1050 867 T 1069 825 Q 1069 813 1067 809 L 920 219 Q 905 161 905 119 Q 905 31 965 31 Q 1029 31 1061 110 T 1118 301 Q 1122 313 1135 313 H 1159 Q 1167 313 1172 306 T 1178 293 Q 1142 150 1099 63 T 961 -23 Q 893 -23 840 16 T 772 121 Q 721 57 652 17 T 508 -23 Q 380 -23 307 37 L 203 -377 Q 197 -406 173 -424 T 119 -442 Q 94 -442 75 -427 T 57 -385 Z "/></g><g transform="translate(79.05900, -11.44501)"><path transform="scale(0.48828, -0.48828)" d="M 276 1102 L 236 1145 L 512 1421 L 786 1145 L 745 1102 L 512 1307 L 276 1102 Z "/></g></g><path transform="matrix(0.48828, 0.00000, 0.00000, -0.48828, 602.56000, 0.00000)" d="M 635 -508 Q 521 -418 438 -302 T 303 -53 T 225 223 T 199 512 Q 199 659 225 803 T 304 1080 T 441 1329 T 635 1532 Q 635 1536 645 1536 H 664 Q 670 1536 675 1530 T 680 1518 Q 680 1509 676 1505 Q 576 1407 509 1295 T 402 1056 T 344 794 T 326 512 Q 326 -139 674 -477 Q 680 -483 680 -494 Q 680 -499 674 -506 T 664 -512 H 645 Q 635 -512 635 -508 Z "/><path transform="matrix(0.48828, 0.00000, 0.00000, -0.48828, 991.45001, 0.00000)" d="M 172 512 Q 172 541 187 567 T 228 609 T 285 625 Q 314 625 340 609 T 382 567 T 397 512 Q 397 466 364 432 T 285 399 Q 239 399 205 432 T 172 512 Z "/><path transform="matrix(0.48828, 0.00000, 0.00000, -0.48828, 1269.22998, 0.00000)" d="M 203 -369 Q 203 -360 211 -352 Q 285 -281 326 -188 T 367 8 V 33 Q 334 0 285 0 Q 238 0 205 33 T 172 113 Q 172 161 205 193 T 285 225 Q 358 225 389 157 T 420 8 Q 420 -106 374 -209 T 246 -393 Q 238 -397 233 -397 Q 223 -397 213 -388 T 203 -369 Z "/><path transform="matrix(0.48828, 0.00000, 0.00000, -0.48828, 1713.67712, 0.00000)" d="M 178 125 Q 233 31 399 31 Q 471 31 536 55 T 643 129 T 686 248 Q 686 301 648 335 T 555 381 L 444 403 Q 368 422 319 474 T 270 600 Q 270 691 319 761 T 450 868 T 618 905 Q 711 905 784 860 T 858 729 Q 858 682 831 646 T 758 610 Q 731 610 711 627 T 692 672 Q 692 696 705 718 T 741 754 T 788 768 Q 770 812 720 832 T 614 852 Q 562 852 510 831 T 426 769 T 395 674 Q 395 637 421 609 T 485 569 L 604 545 Q 661 533 708 502 T 783 425 T 811 319 Q 811 243 768 169 T 664 51 Q 555 -23 397 -23 Q 288 -23 197 27 T 106 176 Q 106 232 138 273 T 227 315 Q 260 315 282 295 T 305 242 Q 305 195 270 160 T 188 125 H 178 Z "/><path transform="matrix(0.48828, 0.00000, 0.00000, -0.48828, 2182.45703, 0.00000)" d="M 133 -512 Q 115 -512 115 -494 Q 115 -485 119 -481 Q 469 -139 469 512 Q 469 1163 123 1501 Q 115 1506 115 1518 Q 115 1525 120 1530 T 133 1536 H 152 Q 158 1536 162 1532 Q 309 1416 407 1250 T 550 896 T 596 512 Q 596 367 571 226 T 493 -51 T 358 -303 T 162 -508 Q 158 -512 152 -512 H 133 Z "/></g></g></svg>. With a crystal ball, one could pick the predictor in <svg class="gs_fsvg" aria-label="{\hat{\mu}(\cdot, s)}" width="36px" height="14px" style="vertical-align:-4px;"><g transform="matrix(0.01400, 0.00000, 0.00000, 0.01400, 0.00000, 10.50000)"><g><g><g><path transform="scale(0.48828, -0.48828)" d="M 57 -385 Q 57 -373 59 -369 L 360 840 Q 369 869 392 887 T 446 905 Q 472 905 490 889 T 508 848 Q 508 842 507 838 T 506 831 L 385 352 Q 365 267 365 205 Q 365 129 401 80 T 510 31 Q 659 31 772 217 Q 773 219 773 220 T 774 223 L 922 817 Q 929 844 954 863 T 1008 883 Q 1032 883 1050 867 T 1069 825 Q 1069 813 1067 809 L 920 219 Q 905 161 905 119 Q 905 31 965 31 Q 1029 31 1061 110 T 1118 301 Q 1122 313 1135 313 H 1159 Q 1167 313 1172 306 T 1178 293 Q 1142 150 1099 63 T 961 -23 Q 893 -23 840 16 T 772 121 Q 721 57 652 17 T 508 -23 Q 380 -23 307 37 L 203 -377 Q 197 -406 173 -424 T 119 -442 Q 94 -442 75 -427 T 57 -385 Z "/></g><g transform="translate(79.05900, -11.44501)"><path transform="scale(0.48828, -0.48828)" d="M 276 1102 L 236 1145 L 512 1421 L 786 1145 L 745 1102 L 512 1307 L 276 1102 Z "/></g></g><path transform="matrix(0.48828, 0.00000, 0.00000, -0.48828, 602.56000, 0.00000)" d="M 635 -508 Q 521 -418 438 -302 T 303 -53 T 225 223 T 199 512 Q 199 659 225 803 T 304 1080 T 441 1329 T 635 1532 Q 635 1536 645 1536 H 664 Q 670 1536 675 1530 T 680 1518 Q 680 1509 676 1505 Q 576 1407 509 1295 T 402 1056 T 344 794 T 326 512 Q 326 -139 674 -477 Q 680 -483 680 -494 Q 680 -499 674 -506 T 664 -512 H 645 Q 635 -512 635 -508 Z "/><path transform="matrix(0.48828, 0.00000, 0.00000, -0.48828, 991.45001, 0.00000)" d="M 172 512 Q 172 541 187 567 T 228 609 T 285 625 Q 314 625 340 609 T 382 567 T 397 512 Q 397 466 364 432 T 285 399 Q 239 399 205 432 T 172 512 Z "/><path transform="matrix(0.48828, 0.00000, 0.00000, -0.48828, 1269.22998, 0.00000)" d="M 203 -369 Q 203 -360 211 -352 Q 285 -281 326 -188 T 367 8 V 33 Q 334 0 285 0 Q 238 0 205 33 T 172 113 Q 172 161 205 193 T 285 225 Q 358 225 389 157 T 420 8 Q 420 -106 374 -209 T 246 -393 Q 238 -397 233 -397 Q 223 -397 213 -388 T 203 -369 Z "/><path transform="matrix(0.48828, 0.00000, 0.00000, -0.48828, 1713.67712, 0.00000)" d="M 178 125 Q 233 31 399 31 Q 471 31 536 55 T 643 129 T 686 248 Q 686 301 648 335 T 555 381 L 444 403 Q 368 422 319 474 T 270 600 Q 270 691 319 761 T 450 868 T 618 905 Q 711 905 784 860 T 858 729 Q 858 682 831 646 T 758 610 Q 731 610 711 627 T 692 672 Q 692 696 705 718 T 741 754 T 788 768 Q 770 812 720 832 T 614 852 Q 562 852 510 831 T 426 769 T 395 674 Q 395 637 421 609 T 485 569 L 604 545 Q 661 533 708 502 T 783 425 T 811 319 Q 811 243 768 169 T 664 51 Q 555 -23 397 -23 Q 288 -23 197 27 T 106 176 Q 106 232 138 273 T 227 315 Q 260 315 282 295 T 305 242 Q 305 195 270 160 T 188 125 H 178 Z "/><path transform="matrix(0.48828, 0.00000, 0.00000, -0.48828, 2182.45703, 0.00000)" d="M 133 -512 Q 115 -512 115 -494 Q 115 -485 119 -481 Q 469 -139 469 512 Q 469 1163 123 1501 Q 115 1506 115 1518 Q 115 1525 120 1530 T 133 1536 H 152 Q 158 1536 162 1532 Q 309 1416 407 1250 T 550 896 T 596 512 Q 596 367 571 226 T 493 -51 T 358 -303 T 162 -508 Q 158 -512 152 -512 H 133 Z "/></g></g></svg> having minimum prediction error. Without prescience, one uses test sets, cross-validation and so forth. The difference in prediction error between the crystal ball selection and the statistician's choice we call predictive loss. For an unstable procedure the predictive loss is large. This is shown by some analytics in a simple case and by simulation results in a more complex comparison of four different linear regression methods. Unstable procedures can …</div></div></div><div class="gs_scl"><div class="gsc_oci_field">Total citations</div><div class="gsc_oci_value"><div style="margin-bottom:1em"><a href="https://scholar.google.co.in/scholar?oi=bibs&hl=en&cites=7867375791791795936&as_sdt=5">Cited by 1773</a></div><div id="gsc_oci_graph_wrapper"><div id="gsc_oci_graph" style="width:955px;"><div id="gsc_oci_graph_x"></div><div id="gsc_oci_graph_bars"><span class="gsc_oci_g_t" style="left:0px">1996</span><span class="gsc_oci_g_t" style="left:33px">1997</span><span class="gsc_oci_g_t" style="left:66px">1998</span><span class="gsc_oci_g_t" style="left:99px">1999</span><span class="gsc_oci_g_t" style="left:132px">2000</span><span class="gsc_oci_g_t" style="left:165px">2001</span><span class="gsc_oci_g_t" style="left:198px">2002</span><span class="gsc_oci_g_t" style="left:231px">2003</span><span class="gsc_oci_g_t" style="left:264px">2004</span><span class="gsc_oci_g_t" style="left:297px">2005</span><span class="gsc_oci_g_t" style="left:330px">2006</span><span class="gsc_oci_g_t" style="left:363px">2007</span><span class="gsc_oci_g_t" style="left:396px">2008</span><span class="gsc_oci_g_t" style="left:429px">2009</span><span class="gsc_oci_g_t" style="left:462px">2010</span><span class="gsc_oci_g_t" style="left:495px">2011</span><span class="gsc_oci_g_t" style="left:528px">2012</span><span class="gsc_oci_g_t" style="left:561px">2013</span><span class="gsc_oci_g_t" style="left:594px">2014</span><span class="gsc_oci_g_t" style="left:627px">2015</span><span class="gsc_oci_g_t" style="left:660px">2016</span><span class="gsc_oci_g_t" style="left:693px">2017</span><span class="gsc_oci_g_t" style="left:726px">2018</span><span class="gsc_oci_g_t" style="left:759px">2019</span><span class="gsc_oci_g_t" style="left:792px">2020</span><span class="gsc_oci_g_t" style="left:825px">2021</span><span class="gsc_oci_g_t" style="left:858px">2022</span><span class="gsc_oci_g_t" style="left:891px">2023</span><span class="gsc_oci_g_t" style="left:924px">2024</span><a href="https://scholar.google.co.in/scholar?oi=bibs&hl=en&cites=7867375791791795936&as_sdt=5&as_ylo=1996&as_yhi=1996" class="gsc_oci_g_a" style="left:5px;height:7px;top:50px;z-index:29"><span class="gsc_oci_g_al">12</span></a><a href="https://scholar.google.co.in/scholar?oi=bibs&hl=en&cites=7867375791791795936&as_sdt=5&as_ylo=1997&as_yhi=1997" class="gsc_oci_g_a" style="left:38px;height:4px;top:53px;z-index:28"><span class="gsc_oci_g_al">6</span></a><a href="https://scholar.google.co.in/scholar?oi=bibs&hl=en&cites=7867375791791795936&as_sdt=5&as_ylo=1998&as_yhi=1998" class="gsc_oci_g_a" style="left:71px;height:9px;top:48px;z-index:27"><span class="gsc_oci_g_al">15</span></a><a href="https://scholar.google.co.in/scholar?oi=bibs&hl=en&cites=7867375791791795936&as_sdt=5&as_ylo=1999&as_yhi=1999" class="gsc_oci_g_a" style="left:104px;height:16px;top:41px;z-index:26"><span class="gsc_oci_g_al">27</span></a><a href="https://scholar.google.co.in/scholar?oi=bibs&hl=en&cites=7867375791791795936&as_sdt=5&as_ylo=2000&as_yhi=2000" class="gsc_oci_g_a" style="left:137px;height:20px;top:37px;z-index:25"><span class="gsc_oci_g_al">33</span></a><a href="https://scholar.google.co.in/scholar?oi=bibs&hl=en&cites=7867375791791795936&as_sdt=5&as_ylo=2001&as_yhi=2001" class="gsc_oci_g_a" style="left:170px;height:14px;top:43px;z-index:24"><span class="gsc_oci_g_al">23</span></a><a href="https://scholar.google.co.in/scholar?oi=bibs&hl=en&cites=7867375791791795936&as_sdt=5&as_ylo=2002&as_yhi=2002" class="gsc_oci_g_a" style="left:203px;height:17px;top:40px;z-index:23"><span class="gsc_oci_g_al">29</span></a><a href="https://scholar.google.co.in/scholar?oi=bibs&hl=en&cites=7867375791791795936&as_sdt=5&as_ylo=2003&as_yhi=2003" class="gsc_oci_g_a" style="left:236px;height:16px;top:41px;z-index:22"><span class="gsc_oci_g_al">27</span></a><a 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href="https://scholar.google.co.in/scholar?oi=bibs&hl=en&cites=7867375791791795936&as_sdt=5&as_ylo=2024&as_yhi=2024" class="gsc_oci_g_a" style="left:929px;height:38px;top:19px;z-index:1"><span class="gsc_oci_g_al">63</span></a></div></div></div></div></div><div class="gs_scl"><div class="gsc_oci_field">Scholar articles</div><div class="gsc_oci_value"><div class="gsc_oci_merged_snippet"><div><a href="/scholar?oi=bibs&cluster=7867375791791795936&btnI=1&hl=en">Heuristics of instability and stabilization in model selection</a></div><div>L Breiman - The annals of statistics, 1996</div><div><a class="gsc_oms_link" href="https://scholar.google.co.in/scholar?oi=bibs&hl=en&cites=7867375791791795936&as_sdt=5">Cited by 1773</a> <a class="gsc_oms_link" href="https://scholar.google.co.in/scholar?oi=bibs&hl=en&q=related:4O5BeJmILm0J:scholar.google.com/">Related articles</a> <a class="gsc_oms_link" href="https://scholar.google.co.in/scholar?oi=bibs&hl=en&cluster=7867375791791795936">All 14 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