Physical Sciences and Mathematics Commons^™

Bootstrapping Tsmars Models, Liangzhong Chen

Theses

We investigate bootstrap inference methods for nonlinear time series models obtained using Multivariate Adaptive Regression Splines for Time Series (TSMARS), for which theoretical properties are not currently known. We use two different methods of bootstrapping to obtain confidence intervals for the underlying nonlinear function and prediction intervals for future values, based on estimated TSMARS models for the bootstrapped data. We also explore the method of Bootstrap AGGregatING (Bagging), due to Breiman (1996), to investigate whether the residual and prediction mean squared errors from a fitted TSMARS model can be reduced by averaging across the values obtained from each of the …