A regression problem is one where the goal is to predict a single numeric value. For example, you might want to predict the price of a house based on its square footage, age, number of bedrooms and ...

There are roughly a dozen major regression techniques, and each technique has several variations. The most common techniques include linear regression, linear ridge regression, k-nearest neighbors ...

In this article, we propose a generalized Gaussian process concurrent regression model for functional data, where the functional response variable has a binomial. Poisson, or other non-Gaussian ...

The model based on Gaussian process (GP) prior and a kernel covariance function can be used to fit nonlinear data with multidimensional covariates. It has been used as a flexible nonparametric ...