Bringing Bayesian Models to Life empowers the reader to extend, enhance, and implement
statistical models for ecological and environmental data analysis. We open the black box …

Fast and accurate diagnosis is essential for the efficient and effective control of the COVID-
19 pandemic that is currently disrupting the whole world. Despite the prevalence of the …

Joint species distribution models (JSDMs) have gained considerable traction among
ecologists over the past decade, due to their capacity to answer a wide range of questions at …

This volume provides a practical introduction to the method of maximum likelihood as used
in social science research. Ward and Ahlquist focus on applied computation in R and use …

The problem of calibrating deep neural networks (DNNs) for multi-label learning is
considered. It is well-known that DNNs trained by cross-entropy for single-label or one-hot …

Class imbalance is a common issue in many applications such as medical diagnosis, fraud
detection, web advertising, etc. Although standard deep learning method has achieved …

We introduce a binary regression accounting-based model for bankruptcy prediction of small
and medium enterprises (SMEs). The main advantage of the model lies in its predictive …

The paper proposes a novel model for the prediction of bank failures, on the basis of both
macroeconomic and bank-specific microeconomic factors. As bank failures are rare, in the …

This paper describes novel approaches to predict default for SMEs. Multivariate outlier
detection techniques based on Local Outlier Factor are proposed to improve the out of …

Class imbalance, where there are great differences between the number of observations
associated with particular discrete outcomes, is common within transportation and other …