Testing parametric models in linear‐directional regression

Testing parametric models in linear-directional regression

This paper presents a goodness-of-fit test for parametric regression models with scalar response and directional predictor, that is, vectors in a sphere of arbitrary dimension. The testing procedure is based on the weighted squared distance between a smooth and a parametric regression estimator, where the smooth regression estimator is obtained by a projected local approach. Asymptotic behavior...

Testing Lack-of-fit of Parametric Regression Models Using Nonparametric Regression Techniques

Data-driven lack-of-fit tests are derived for parametric regression models using fit comparison statistics that are based on nonparametric linear smoothers. The tests are applicable to settings where the usual bandwidth/smoothing parameter asymptotics apply to the null model, which includes testing for nonlinear models and some linear models. Large sample distribution theory is established for ...

Smoothing Spline Semi-parametric Nonlinear Regression Models

We consider the problem of modeling the mean function in regression. Often there is enough knowledge to model some components of the mean function parametrically. But for other vague and/or nuisance components, it is often desirable to leave them unspecified and to be modeled nonparametrically. In this article, we propose a general class of smoothing spline semi-parametric nonlinear regression ...

Predictive Interval Models for Non-parametric Regression

Having a regression model, we are interested in finding two-sided intervals that are guaranteed to contain at least a desired proportion of the conditional distribution of the response variable given a specific combination of predictors. We name such intervals predictive intervals. This work presents a new method to find two-sided predictive intervals for non-parametric least squares regression...

Testing Regression Models With Fewer