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- W2989725025 abstract "The bootstrap method for re-sampling essentially obtained the re- sampled observations from the empirical distribution function of the original data. The method relied heavily on the assumption of independence of the ob- servations (iid). When the original data were correlated, then the usual boot- strap technique might fail to give appropriate re-sampled data. This study pro- posed a new method for generating bootstrap observations from dependent observations knowing the original correlation structure of the data. Independ- ent and identically distributed initial bootstrap samples were obtained from the empirical cumulative distribution function of the data. The bootstrap re- samples from the original data are obtained from the space generated by the initial bootstrap subsamples. It was shown that the correlation structure of the bootstrap samples obtained was the same as the original data. Simulations showed that the relative error and the mean-squared error decrease with in- creasing sample size. However, both types of error increased with increasing dimensionality of a multivariate normal distribution. Keywords : bootstrap, dependent observations, subspace, Cholesky’s method, LU- Decomposition" @default.
- W2989725025 created "2019-12-05" @default.
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- W2989725025 date "2013-01-01" @default.
- W2989725025 modified "2023-09-25" @default.
- W2989725025 title "A Generalized Bootstrap Technique for Dependent Observations" @default.
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