This paper applies a value-based management framework to critically review empirical research in managerial accounting. This framework enables us to place the exceptionally diverse set of managerial accounting studies from the past several decades into an integrated structure. Our synthesis highlights the many consistent results in prior research, identifies remaining gaps and inconsistencies, ...

This paper describes a naturalistic approach to the development of a large-scale, functional, cognitively plausible model of language comprehension, and contrasts the approach with mainstream cognitive modeling and computational linguistic research. An approach to research is described which accepts the theoretical constraints of a cognitive architecture, while replacing the fine-grained empiri...

Methodological norms in economic theorising are interpreted as rational strategies to optimise some epistemic utility functions. A definition of ‘empirical verisimilitude’ is defended as a plausible interpretation of the epistemic preferences of researchers. Some salient differences between the scientific strategies of physics and of economics are derived from the comparison of the relative cos...

A generic fusion problem is studied for multiple sensors whose outputs are probabilistically related to their inputs according to unknown distributions. Sensor measurements are provided as iid input-output samples, and an empirical risk minimization method is described for designing fusers with distribution-free performance bounds. The special cases of isolation and projective fusers for classi...

estimation of reservoir water saturation (sw) is one of the main tasks in well logging. many empirical equations are available, which are, more or less, based on archie equation. the present study is an application of radial basis function neural network (rbfnn) modeling for estimation of water saturation responses in a carbonate reservoir. four conventional petrophysical logs (pls) including d...

In this paper, we study a two-category classification problem. We indicate the categories by labels Y = 1 and Y = −1. We observe a covariate, or feature, X ∈ X ⊂ R. Consider a collection {ha} of classifiers indexed by a finite-dimensional parameter a, and the classifier ha∗ that minimizes the prediction error over this class. The parameter a∗ is estimated by the empirical risk minimizer ân over...

We address the problem of sequential prediction in the heteroscedastic setting, when both the signal and its variance are assumed to depend on explanatory variables. By applying regret minimization techniques, we devise an efficient online learning algorithm for the problem, without assuming that the error terms comply with a specific distribution. We show that our algorithm can be adjusted to ...

There is increasing support for sharing individual-level data generated by medical and public health research. This scoping review of empirical research and conceptual literature examined stakeholders' perspectives of ethical best practices in data sharing, particularly in low- and middle-income settings. Sixty-nine empirical and conceptual articles were reviewed, of which, only five were empir...