First some context. In supervised learning in general, the goal is to learn or infer an (initially) unknown function f : x 7→ y, from a set of training data in the form of T “input/output” pairs {(xμ, yμ)}μ=1:T . 1 More generally, you try to infer the conditional distribution ρ(y|x) from this training set; the reason is that in general your outputs contains some noise (or stated better, trial-t...

BACKGROUND Autoimmune cytopenia (AIC) is a rare complication of allogeneic hematopoietic cell transplantation (HCT). In this study, we reviewed the diagnosis, treatment and response to therapy for pediatric patients with post-HCT AIC at our institution. METHODS Of the 292 allogeneic HCTs performed from January, 2011 to December, 2015 at the Department of Pediatrics, The Catholic University of...

ABSTRACT In the present paper, we consider the variable selection problem in Poisson regression models. Akaike’s information criterion (AIC) is the most commonly applied criterion for selecting variables. However, the bias of the AIC cannot be ignored, especially in small samples. We herein propose a new bias-corrected version of the AIC that is constructed by stochastic expansion of the maximu...

The purpose of the present study was to evaluate the mechanical and chemical properties of a novel glass ionomer cement for use as a pit and fissure sealant containing a porous hydroxyapatite, namely, apatite ionomer cement (AIC). Control sealant samples were used Fuji III (GIC-S). The experiment sealant samples (AIC-S) consisted of porous spherical hydroxyapatite (HApS) particles added at 28 w...

It is well known that AIC and BIC have different properties in model selection. BIC is consistent in the sense that if the true model is among the candidates, the probability of selecting the true model approaches 1. On the other hand, AIC is minimax-rate optimal for both parametric and nonparametric cases for estimating the regression function. There are several successful results on construct...

Akaike s Information Criterion AIC derived from asymptotics of the maximum like lihood estimator is widely used in model selection However it has a nite sample bias which produces over tting in linear regression To deal with this problem Ishiguro et al proposed a bootstrap based extension to AIC which they call EIC In this report we compare model selection performance of AIC EIC a bootstrap smo...

It has been shown that AIC-type criteria are asymptotically efficient selectors of the tuning parameter in non-concave penalized regression methods under the assumption that the population variance is known or that a consistent estimator is available. We relax this assumption to prove that AIC itself is asymptotically efficient and we study its performance in finite samples. In classical regres...