between error estimation and data-based complexity penalization: any good error estimate may be converted into a data …

motion (GBM) process. That is, we show what difficulties can arise when failing to account for estimation risk. Our working … parameter sigma, we derive the resulting marginal distribution of the approximated GBM. This allows us to derive post-estimation … results for the technically more-complicated formulae associated with our post-estimation process. We discuss an assortment of …

estimation error and a communication cost. In our formulation, the transmission of a real number from the pre-processor to the …

Estimation in nonlinear time series models has mainly been performed by least squares or maximum likelihood (ML … respect. On the other hand, the three estimation techniques lead to fairly similar power functions for a linearity test. …