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Neural networks are used for prediction model in many applications. The backpropagation algorithm used in most cases corresponds to a statistical nonlinear regression model assuming the constant noise level. Many proposed prediction intervals in the literature so far also assume the constant noise level. There are no prediction intervals in the literature… (More)

Bivariate survival analysis has wide applications. In the presence of covariates, most literature focuses on studying their effects on the marginal distributions. However covariates can also affect the association between the two variables. In this article we consider the latter issue by proposing a nonstandard local linear estimator for the concordance… (More)

Very often the input variables for neural-network predictions contain measurement errors. In particular, this may happen because the original input variables are often not available at the time of prediction and have to be replaced by predicted values themselves. This issue is usually ignored and results in nonoptimal predictions. This paper shows that… (More)

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