id: 06118038
dt: j
an: 06118038
au: Neath, Andrew A.; Cavanaugh, Joseph E.; Riedle, Benjamim
ti: A bootstrap method for assessing uncertainty in Kullback-Leibler
    discrepancy model selection problems.
so: Math. Eng. Sci. Aerosp. MESA 3, No. 4, 381-391 (2012).
py: 2012
pu: Cambridge Scientific Publishers, Cambridge; I \& S Publishers, Daytona
    Beach, FL
la: EN
cc: 
ut: Akaike information criterion; Akaike weights; bootstrap AIC; bootstrap
    weights; extended information criterion; variable selection
ci: 
li: http://nonlinearstudies.com/index.php/mesa/article/view/807/516
ab: Summary: The goal of a model selection problem is to determine which model
    from a candidate collection best approximates the true or generating
    model, where separation between the true model and an approximating
    model is measured by a discrepancy function. The contribution of the
    current paper is to provide a unique approach to assessing the
    uncertainty inherent to a model selection problem. We define bootstrap
    weights as a calculation of the probability on each model in the
    candidate collection being closest to the true model, as measured by
    the Kullback-Leibler discrepancy. Examples are presented to show that
    these bootstrap weights provide new and important information to aid
    model selection practitioners
rv: