2021
DOI: 10.48550/arxiv.2109.10451
|View full text |Cite
Preprint
|
Sign up to set email alerts
|

More powerful selective inference for the graph fused lasso

Abstract: The graph fused lasso -which includes as a special case the one-dimensional fused lasso -is widely used to reconstruct signals that are piecewise constant on a graph, meaning that nodes connected by an edge tend to have identical values. We consider testing for a difference in the means of two connected components estimated using the graph fused lasso. A naive procedure such as a z-test for a difference in means will not control the selective Type I error, since the hypothesis that we are testing is itself a f… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
1
0

Year Published

2021
2021
2021
2021

Publication Types

Select...
1

Relationship

0
1

Authors

Journals

citations
Cited by 1 publication
(1 citation statement)
references
References 19 publications
0
1
0
Order By: Relevance
“…In some specific settings, data carving approaches may also offer a viable path forward. When the (fused or generalized) lasso is used to select the choice of knots, methods such as those described in Chen et al (2021), Duy andTakeuchi (2021), andHyun et al (2018) can be used to construct test and confidence intervals with valid post-selective distributions. Although tests constructed through these methodologies will likely have higher power than a data blurring approach due to conditioning on a smaller amount of information, the drawback of these approaches is that they offer the analyst very little flexibility during the selection stage.…”
Section: A Recap Of Trend Filteringmentioning
confidence: 99%
“…In some specific settings, data carving approaches may also offer a viable path forward. When the (fused or generalized) lasso is used to select the choice of knots, methods such as those described in Chen et al (2021), Duy andTakeuchi (2021), andHyun et al (2018) can be used to construct test and confidence intervals with valid post-selective distributions. Although tests constructed through these methodologies will likely have higher power than a data blurring approach due to conditioning on a smaller amount of information, the drawback of these approaches is that they offer the analyst very little flexibility during the selection stage.…”
Section: A Recap Of Trend Filteringmentioning
confidence: 99%