We investigate nonparametric regression methods based on spatial depth and quantiles when the response and the covariate are both functions. As in classical quantile regression for finite dimensional ...

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.

We apply nonparametric regression models to estimation of demand curves of the type most often used in applied research. From the demand curve estimators we derive estimates of exact consumers surplus ...

Abstract: For a system with inputs and outputs, a nonparametric regression has been proposed to clarify the relationship between inputs and outputs from a large amount of data. To improve estimation ...

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 ...

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, ...