TY - JOUR
T1 - Bootstrapping weighted empirical processes that do not converge weakly
AU - Lahiri, Soumendra Nath
PY - 1998/3/16
Y1 - 1998/3/16
N2 - We show that the bootstrap method provides valid approximations to the sampling distribution of a weighted empirical process on D[0,1] even in the cases where it fails to converge weakly. Furthermore, the result is applied to construct valid bootstrap confidence sets in such pathological cases.
AB - We show that the bootstrap method provides valid approximations to the sampling distribution of a weighted empirical process on D[0,1] even in the cases where it fails to converge weakly. Furthermore, the result is applied to construct valid bootstrap confidence sets in such pathological cases.
KW - Bootstrap
KW - Confidence sets
KW - Weak convergence
KW - Weighted empirical process
UR - https://www.scopus.com/pages/publications/0032536671
U2 - 10.1016/s0167-7152(97)84156-9
DO - 10.1016/s0167-7152(97)84156-9
M3 - Article
AN - SCOPUS:0032536671
SN - 0167-7152
VL - 37
SP - 295
EP - 302
JO - Statistics and Probability Letters
JF - Statistics and Probability Letters
IS - 3
ER -