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, ...

Several empirical methods for flood flow rate estimation have been presented. In this study,eight new empirical runoff estimation method including Dredge & Burge, Bourges, Inglis, Faning, Hyderabad, Burkli Ziegler, Cramar and Possenti were calculated in Bakhtegan watershed which is located in Fars province. After studying empirical methods for annual flood flow rate estimation and comparing the...

Several empirical methods for flood flow rate estimation have been presented. In this study,eight new empirical runoff estimation method including Dredge & Burge, Bourges, Inglis, Faning, Hyderabad, Burkli Ziegler, Cramar and Possenti were calculated in Bakhtegan watershed which is located in Fars province. After studying empirical methods for annual flood flow rate estimation and comparing the...

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...

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...

This paper proves, in very general settings, that convex risk minimization is a procedure to select a unique conditional probability model determined by the classification problem. Unlike most previous work, we give results that are general enough to include cases in which no minimum exists, as occurs typically, for instance, with standard boosting algorithms. Concretely, we first show that any...