ArticleJournal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America2019
Fast Bootstrap Confidence Intervals for Continuous Threshold Linear Regression.
Article in Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- The global burden of heart failure attributable to interstitial lung diseases: insights from the global burden of disease study 2021.BMC cardiovascular disorders · 2025Article
- Trend analysis and age-period-cohort effects on morbidity and mortality of liver cancer from 2010 to 2020 in Guangzhou, China.Frontiers in oncology · 2024Article
- L-Lysine supplementation affects dietary protein quality and growth and serum amino acid concentrations in rats.Scientific reports · 2023Article
- Fast Grid Search and Bootstrap-based Inference for Continuous Two-phase Polynomial Regression Models.Environmetrics · 2021Article
- Estimation and Inference for Upper Hinge Regression Models.Environmental and ecological statistics · 2019Article
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1 author.
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Abstract
Continuous threshold regression is a common type of nonlinear regression that is attractive to many practitioners for its easy interpretability. More widespread adoption of thresh-old regression faces two challenges: (i) the computational complexity of fitting threshold regression models and (ii) obtaining correct coverage of confidence intervals under model misspecification. Both challenges result from the non-smooth and non-convex nature of the threshold regression model likelihood function. In this paper we first show that these two issues together make the ideal approach for making model-robust inference in continuous threshold linear regression an impractical one. The need for a faster way of fitting continuous threshold linear models motivated us to develop a fast grid search method. The new method, based on the simple yet powerful dynamic programming principle, improves the performance by several orders of magnitude.
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