Non-homogeneous regression models are widely used to statistically post-process numerical ensemble weather prediction models. Such regression models are capable of forecasting full probability distributions and correct for ensemble errors in the mean and variance. To estimate the corresponding...

This paper establishes a Durbin-Watson test statistic with sufficiency and rebuilds the probability table for hypothesis testing in the multivariate regression model without intercept because the Durbin-Watson test which has numerous problems first established in a multiple regression model with...

This paper revalidates the Durbin-Watson test statistic and rebuilds the probability table for hypothesis testing in the multivariate regression model without intercept because the Durbin-Watson test which has numerous problems. The paper shows that the independent variables, hypothesized...

This paper establishes a Durbin-Watson test statistic with sufficiency and rebuilds the probability table for hypothesis testing in the multivariate regression model with intercept because the Durbin-Watson test which has numerous problems first established in a multiple regression model with...

This paper proposes a simple maximum likelihood regression estimator that outperforms Least Squares in terms of efficiency and mean square error for a large number of skewed and/or heavy tailed error distributions

Estimators of regression coefficients are known to be asymptotically normally distributed, provided certain regularity conditions are satisfied. In small samples and if the noise is not normally distributed, this can be a poor guide to the quality of the estimators. The paper addresses this...

This paper presents a regression procedure for inhomogeneous data characterized by varying variance, skewness and kurtosis or by an unequal amount of data over the estimation domain. The concept is based first on the estimation of the densities of an observed variable for given values of...

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...

In this article, we propose a multivariate Pascal mixture regression model as an alternative to understand the association between multivariate count response variables and their covariates. When compared to the copula approach, this proposed class of regression models is not only less complex...

We study the problem of estimating the parameters of a linear median regression without any assumption on the shape of the error distribution -- including no condition on the existence of moments -- allowing for heterogeneity (or heteroskedasticity) of unknown form, noncontinuous distributions,...