Testing with a nuisance parameter present only under the alternative: a score-based approach with application to segmented modelling

By Vito M. R. Muggeo. 2016

143 citations recorded by the source corpus.

ABSTRACT We introduce a score-type statistic to test for a non-zero regression coefficient when the relevant term involves a nuisance parameter present only under the alternative. Despite the non-regularity and complexity of the problem and unlike the previous approaches, the proposed test statistic does not require the nuisance to be estimated. It is simple to implement by relying on the conventional distributions, such as Normal or t, and it justified in the setting of probabilistic coherence. We focus on testing for the existence of a breakpoint in segmented regression, and illustrate the methodology with an analysis on data of DNA copy number aberrations and gene expression profiles from 97 breast cancer patients; moreover some simulations reveal that the proposed test is more powerful than its competitors previously discussed in literature.

Subfields: Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/a71e3fe2f4a88c0f35191c1f99eb8aa371dccbd4

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Statistics and Probability

Citations in the corpus

Making AI Forget You: Data Deletion in Machine Learning (2019, methodology): “Segmented Regression Segmented (or piece-wise) linear regression is a common relaxation of canonical regression models [58, 59, 57].”