Inference for Optimal Dynamic Treatment Regimes Using an Adaptive m ‐Out‐of‐ n Bootstrap Scheme-Reference-Cited by-同舟云学术

Inference for Optimal Dynamic Treatment Regimes Using an Adaptive m ‐Out‐of‐ n Bootstrap Scheme

Published:2013-07-11 Issue:3 Volume:69 Page:714-723
ISSN:0006-341X
Container-title:Biometrics
language:en
Short-container-title:Biometrics

Author:

Chakraborty Bibhas¹,Laber Eric B.²,Zhao Yingqi³

Affiliation:

1. Department of BiostatisticsColumbia UniversityNew YorkNew York 10032U.S.A.

2. Department of StatisticsNorth Carolina State University, RaleighNorth Carolina 27695U.S.A.

3. Department of Biostatistics and Medical InformaticsUniversity of Wisconsin‐Madison, MadisonWisconsin 53792U.S.A.

Publisher

Wiley

Subject

Applied Mathematics,General Agricultural and Biological Sciences,General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,General Medicine,Statistics and Probability

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/biom.12052

Reference42 articles.

1. Bellman R.(1957).Dynamic programming. Princeton University Press Princeton.

2. Some Asymptotic Theory for the Bootstrap

3. Resampling fewer than n observations: gains, losses and remedies for losses;Bickel P.;Statistica Sinica,1997

4. On the choice of m in the m out of n bootstrap and confidence bounds for extrema;Bickel P.;Statistica Sinica,2008

5. Bretagnolle J.(1983).Lois limites du bootstrap de certaines fonctionnelles. In Annales de l'IHP Probabilités et statistiques volume 19 pages281–296. Elsevier.

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