Point process modeling is gaining increasing attention, as point type data are emerging in a large variety of scientific applications. In this article, motivated by neuronal spike trains study, we propose novel regression model, where both the response and predictor can be high-dimensional process. We model effects through conditional intensities using set basis transferring functions convoluti...

Distributional regression is extended to Gaussian response vectors of dimension greater than two by parameterizing the covariance matrix $\Sigma$ distribution using entries its Cholesky decomposition. The more common variance-correlation parameterization limits such regressions bivariate responses -- higher dimensions require complicated constraints among correlations ensure positive definite a...

In multivariate regression, interest lies on how the response vector depends a set of covariates. A regression model is proposed where covariates explain variation in only direction first principal component axis. This not parsimonious, but it provides an easy interpretation allometric growth studies log-transformed data corresponds to constants growth. The naturally generalizes two–group exten...

The goal of this study is to introduce a statistical method regarding the analysis of specific latent data for regression analysis of the discrete data and to build a relation between a probit regression model (related to the discrete response) and normal linear regression model (related to the latent data of continuous response). This method provides precise inferences on binary and multinomia...