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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
The Weighted Bootstrap

The Weighted Bootstrap

Barbe, Philippe
Bertail, Patrice

Paperback

Series: Lecture Notes in Statistics, Book 98

Probability & Statistics

ISBN10: 0387944788
ISBN13: 9780387944784
Publisher: Springer
Published: Feb 24 1995
Pages: 230
Weight: 0.76
Height: 0.51 Width: 6.14 Depth: 9.21
Language: English
INTRODUCTION 1) Introduction In 1979, Efron introduced the bootstrap method as a kind of universal tool to obtain approximation of the distribution of statistics. The now well known underlying idea is the following: consider a sample X of Xl ' n independent and identically distributed H.i.d.) random variables (r. v, 's) with unknown probability measure (p.m.) P . Assume we are interested in approximating the distribution of a statistical functional T(P ) the -1 nn empirical counterpart of the functional T(P), where P n: = n l: i=l aX. is 1 the empirical p.m. Since in some sense P is close to P when n is large, n - - LLd. from P and builds the empirical p.m. if one samples Xl ' ..., Xm n n -1 mn - - P T(P ) conditionally on: = mn l: i =1 a - ' then the behaviour of P m n, m n n n X. 1 T(P ) should imitate that of when n and mn get large. n This idea has lead to considerable investigations to see when it is correct, and when it is not. When it is not, one looks if there is any way to adapt it.

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