论文信息 - Standard Error Estimation in Neural Network Regression Models: the AR-Sieve Bootstrap Approach

Standard Error Estimation in Neural Network Regression Models: the AR-Sieve Bootstrap Approach

In this paper we investigate the usage of the AR-Sieve bootstrap method to estimate the standard error of the sampling distribution of the neural network predictive values in a regression model with dependent errors. The performance of the proposed approach is evaluated by a Monte Carlo experiment where it is also compared with the classical residual bootstrap scheme.

Francesco Giordano | Michele La Rocca | Cira Perna

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