Balanced bootstrap resampling method for neural model selection

Balanced bootstrap resampling method for neural model selection

Uniform resampling is the easiest to apply and is a general recipe for all problems, but it may require a large replication size B. To save computational effort in uniform resampling, balanced bootstrap resampling is proposed to change the bootstrap resampling plan. This resampling plan is effective for approximating the center of the bootstrap distribution. Therefore, this paper applies it to ...

Bootstrap for neural model selection

Bootstrap techniques (also called resampling computation techniques) have introduced new advances in modeling and model evaluation [10]. Using resampling methods to construct a series of new samples which are based on the original data set, allows to estimate the stability of the parameters. Properties such as convergence and asymptotic normality can be checked for any particular observed data ...

Weighted bootstrap for neural model selection

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Efficient Bootstrap Resampling

Bootstrap principle is briefly reviewed. Hall’s (1989) antithetic variates method for bootstrap is discussed and extended to more than two antithetic resampling processes. We illustrate the theory with a simulation study. The numerical results show that increasing the number of antithetic resampling processes produces significant smaller variances of the bootstrap estimator over the paired case.

- multiscale bootstrap resampling