… which the estimation of the network weights is performed. The first step in the estimation
process of … Our estimation approach provides objectivity to the estimation and reduces the bias. …

… Different traditional statistical models, including regression, nearest neighbor, and multiple
discriminant analysis, were not given significant results as compared to machine learning …

… narrative analysis for two default episodes in richer detail and argue that the flexibility of our
accounting framework allows us to summarize the salient features of these episodes. Some …

… variety of methods for the estimation of default probabilities; linear discriminant analysis and
logistic regression are the most popular. The linear discriminant analysis (LDA) provides an …

… the effect of loan default rate on financial performance of SACCOs in Narok County. Specifically
the study sought to determine the effect of loan default rate on financial performance of …

… the sensitivity analysis of the endogenous factors such as pledge rate and default loss on …
, namely pledge rate and default loss, this paper conducts sensitivity analysis on the system …

… Hence, as part of our identification strategy, we address these endogeneity concerns in
three ways. First, we employ lagged independent variables to control for reverse causality. …

… of Ensemble Boosting approaches, to determine the accuracy of our predictive model's …
As a result of the bank loan default study utilized in this article, default goal values are binary…

… Our second contribution is an analysis of the influence of leverage on the probability of
recovery from the state of insolvency. Here, as in the Orbis database, “Insolvency” is used to refer …

… study is to determine the values of misclassification costs by incorporating profit losses and
other … We adopt a quadratic programming to deduce loss given default (Lgd) backward from …