Structure selection with ANOVA: local linear models

Abstract

The structure identification problem when estimating local linear models can be eased by using Analysis of Variance (ANOVA) as a prior step in the estimation procedure. The information gained from using ANOVA on the input/output data is what regressors that should be used to partition the input space and what regressors are needed only for the linear models in each part. Also the complexity of the partitioning can be restricted due to the extra information. Abstract: The structure identification problem when estimating local linear models can be eased by using Analysis of Variance (ANOVA) as a prior step in the estimation procedure. The information gained from using ANOVA on the input/output data is what regressors that should be used to partition the input space and what regressors are needed only for the linear models in each part. Also the complexity of the partitioning can be restricted due to the extra information. Keywords

Nonlinear Structure Identification With Linear Least Squares and Anova

“…ANOVA (Miller, 1997;Montgomery, 1991) can be used for finding proper regressors and model structure for a nonlinear model by fitting a locally constant model to the response surface of the data (Lind, 2000(Lind, , 2002Lind and Ljung, 2003). A clever parameterisation of a locally constant model makes it possible to perform hypothesis tests in a balanced and computationally very effective way.…”