# Testing against a linear regression model using ideas from shape-restricted estimation

arXiv: https://arxiv.org/abs/1311.6849 · HTML: https://mlsanity.com/paper/1311.6849/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Nov 2013.

## Built on by (most cited first, 1 of 1)

- [GBM-based Bregman Proximal Algorithms for Constrained Learning](https://mlsanity.com/paper/2308.10767/) (2023)

## Abstract (arXiv)

A formal likelihood ratio hypothesis test for the validity of a parametric regression function is proposed, using a large-dimensional, nonparametric double cone alternative. For example, the test against a constant function uses the alternative of increasing or decreasing regression functions, and the test against a linear function uses the convex or concave alternative. The proposed test is exact, unbiased and the critical value is easily computed. The power of the test increases to one as the sample size increases, under very mild assumptions -- even when the alternative is mis-specified. That is, the power of the test converges to one for any true regression function that deviates (in a non-degenerate way) from the parametric null hypothesis. We also formulate tests for the linear versus partial linear model, and consider the special case of the additive model. Simulations show that our procedure behaves well consistently when compared with other methods. Although the alternative fit is non-parametric, no tuning parameters are involved.