Linear models, generalized linear models, and nonlinear models are examples of parametric regression models because we know the function that describes the relationship between the response and ...

Average derivative functionals of regression are proposed for nonparametric model selection and diagnostics. The functionals are of the integral type, which under certain conditions allows their ...

Nonparametric regression for functional data provides a flexible statistical framework for modelling relationships between a scalar response and predictors that are inherently functional in nature.

The Annals of Statistics, Vol. 31, No. 3 (Jun., 2003), pp. 880-920 (41 pages) We propose a new test for the comparison of two regression curves that is based on a difference of two marked empirical ...

In this module, we will introduce generalized linear models (GLMs) through the study of binomial data. In particular, we will motivate the need for GLMs; introduce the binomial regression model, ...

Nonparametric estimation and U-statistics have emerged as vital tools in modern statistical analysis, offering robust alternatives to traditional parametric methods. Nonparametric techniques bypass ...

Bayesian field theory denotes a nonparametric Bayesian approach for learning functions from observational data. Based on the principles of Bayesian statistics, a particular Bayesian field theory is ...