Fitting high-dimensional statistical models often requires the use of non-linear parameter
estimation procedures. As a consequence, it is generally impossible to obtain an exact …

A class of predictive densities is derived by weighting the observed samples in maximizing
the log-likelihood function. This approach is effective in cases such as sample surveys or …

To realize an input-output relation given by noise-contaminated examples, it is effective to
use a stochastic model of neural networks. When the model network includes hidden units …

The task of statistical model selection is to choose a family of distributions among a possible
set of families, which is the best approximation of reality manifested in the observed data. In …

Precise spike coordination between the spiking activities of multiple neurons is suggested
as an indication of coordinated network activity in active cell assemblies. Spike correlation …

Abstract Model selection and model averaging are two important techniques to obtain
practical and useful models in applied research. However, it is now well-known that many …

We derive and investigate a variant of AIC, the Akaike information criterion, for model
selection in settings where the observed data is incomplete. Our variant is based on the …

The Akaike information criterion, AIC, is one of the most frequently used methods to select
one or a few good, optimal regression models from a set of candidate models. In case the …

Application of classical model selection methods such as Akaike's information criterion (AIC)
becomes problematic when observations are missing. In this article we propose some …

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