Analysis of variance, coefficient of determination and $F$-test for local polynomial regression

TL;DR

This paper develops an ANOVA framework for local polynomial regression, introducing local and global R-squared measures, an $F$-test for effect significance, and extending the approach to varying coefficient models.

Contribution

It provides the first exact local ANOVA decomposition for LPR, defines new R-squared and $F$-test tools, and extends these methods to varying coefficient models.

Findings

Local ANOVA decomposition is exact and simple.

Proposed R-squared quantifies local variation explained.

An $F$-test for no effect is asymptotically valid.

Abstract

This paper provides ANOVA inference for nonparametric local polynomial regression (LPR) in analogy with ANOVA tools for the classical linear regression model. A surprisingly simple and exact local ANOVA decomposition is established, and a local R-squared quantity is defined to measure the proportion of local variation explained by fitting LPR. A global ANOVA decomposition is obtained by integrating local counterparts, and a global R-squared and a symmetric projection matrix are defined. We show that the proposed projection matrix is asymptotically idempotent and asymptotically orthogonal to its complement, naturally leading to an $F$-test for testing for no effect. A by-product result is that the asymptotic bias of the ``projected'' response based on local linear regression is of quartic order of the bandwidth. Numerical results illustrate the behaviors of the proposed R-squared and $F$-test. The ANOVA methodology is also extended to varying coefficient models.