In explaining wage or income by personal attributes (e.g. educational attainment, age, and ethnicity) in a regression model, many researchers choose to use the log of wage or income as the dependent variable and then to estimate the unknown coefficients by some version of the least-squares...

Log-linear and log-log regressions are one of the most used statistical models. However, handling zeros in the dependent and independent variable has remained obscure despite the prevalence of the situation. In this paper, we discuss how to deal with this issue. We show that using Pseudo-Poisson...

Non-homogeneous post-processing is often used to improve the predictive performance of probabilistic ensemble forecasts. A common quantity to develop, test, and demonstrate new methods is the near-surface air temperature frequently assumed to follow a Gaussian response distribution. However,...

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...