In Defense of the Epistemic Imperative

Sample (2015) argues that scientists ought not to believe that their theories are true because they cannot fulfill the epistemic obligation to take the diachronic perspective on their theories. I reply that Sample’s argument imposes an inordinately heavy epistemic obligation on scientists, and that it spells doom not only for scientific theories but also for observational beliefs and philosophical ideas that Samples endorses. I also delineate what I take to be a reasonable epistemic obligation for scientists. In sum, philosophers ought to impose on scientists only an epistemic standard that they are willing to impose on themselves

L2L_2 boosting in kernel regression

In this paper, we investigate the theoretical and empirical properties of $L_2$ boosting with kernel regression estimates as weak learners. We show that each step of $L_2$ boosting reduces the bias of the estimate by two orders of magnitude, while it does not deteriorate the order of the variance. We illustrate the theoretical findings by some simulated examples. Also, we demonstrate that $L_2$ boosting is superior to the use of higher-order kernels, which is a well-known method of reducing the bias of the kernel estimate.Comment: Published in at http://dx.doi.org/10.3150/08-BEJ160 the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm