A robust estimation for the extended t-process regression model

12/18/2018

∙

Robust estimation and variable selection procedure are developed for the extended t-process regression model with functional data. Statistical properties such as consistency of estimators and predictions are obtained. Numerical studies show that the proposed method performs well.

READ FULL TEXT

A robust estimation for the extended t-process regression model

Robust functional ANOVA model with t-process

Determining input variable ranges in Industry 4.0: A heuristic for estimating the domain of a real-valued function or trained regression model given an output range

Reluctant Interaction Modeling

Variable selection in multivariate regression model for spatially dependent data

On statistical estimation and inferences in optional regression models

The empirical process of residuals from an inverse regression

Supervised learning of a regression model based on latent process. Application to the estimation of fuel cell life time

A robust estimation for the extended t-process regression model

Related Research

Robust functional ANOVA model with t-process

Determining input variable ranges in Industry 4.0: A heuristic for estimating the domain of a real-valued function or trained regression model given an output range

Reluctant Interaction Modeling

Variable selection in multivariate regression model for spatially dependent data

On statistical estimation and inferences in optional regression models

The empirical process of residuals from an inverse regression

Supervised learning of a regression model based on latent process. Application to the estimation of fuel cell life time