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Figure 17 compares the expectation errors estimated with station corrections and with ARV.
Lane departure crashes were additionally caused by errors as a result of masked stimuli, law infringements, expectation errors as well as objective and action slips, while same direction accidents occurred additionally because of focus errors, expectation errors, and objective and action slips.
By contrast, Piotroski and So (2012) document that expectation errors play an important role in explaining the value premium in contrarian investment strategies, and provide evidence consistent with mispricing explanation.
Most of these methods rest on the domain of probabilistic inference and computational learning (Bickel and Doksum 2007; Hernández-Lemus and Rangel-Escareño 2011) and as such, they are subject to expectation errors and asymptotic constraints.
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The expectation error was evaluated for stations where the station correction was obtained.
The expectation error in this study was 0.58 intensity units even when the station correction was used as the site amplification factor.
Note that, for the evaluation of the expectation error, correction of the source term described in Section 4.2 was not applied.
Such a large expectation error would be due to the variance of attenuation and other empirical relations when estimating station corrections and expecting seismic intensities.
The RMS error for the total residual was reduced from 0.68 to 0.58, as shown in Fig. 17(a), which corresponds to a demonstration that using station correction can reduce the expectation error by 15%.
Nevertheless, a noise can be incorporate into the error term due to expectations errors.
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