This paper focuses the development of the diagnostics for the perturbations of case-weights and explanatory variables (one or more) in a linear logistic regression model. The effect of specific perturbation scheme on the estimation of parameters is also assessed. In addition, the interpretation...

We introduce two neural network models designed for application in statistical learning. The mean-variance neural network regression model allows us to simultaneously model the mean and the variance of a response variable. In case of a two-dimensional response vector, the...

This paper illustrates how measurement error (“ME”) in dependent variables not only reduces power but, under common conditions in accounting and finance studies, can lead to statistical biases and erroneous inferences. These confounds exist because ME in accounting-based proxies is typically...