EconPapers    
Economics at your fingertips  
 

Shape Detection Using Semi-Parametric Shape-Restricted Mixed Effects Regression Spline with Applications

Qing Yin, Xiaoshuang Xun, Shyamal D. Peddada () and Jennifer J. Adibi ()
Additional contact information
Qing Yin: University of Pittsburgh
Xiaoshuang Xun: University of Pittsburgh
Shyamal D. Peddada: University of Pittsburgh
Jennifer J. Adibi: University of Pittsburgh

Sankhya B: The Indian Journal of Statistics, 2021, vol. 83, issue 1, No 5, 65-85

Abstract: Abstract Linear models are widely used in the field of epidemiology to model the relationship between placental-fetal hormone and fetal/infant outcome. When a nonlinear relationship is suspected, researchers explore nonparametric models such as regression splines, smoothing splines and penalized regression splines (Korevaar et al., Lancet: Diabetes Endocrinol. 4, 35–43 2016; Wu and Zhang 2006). By applying these nonparametric techniques, researchers can relax the linearity assumption and capture scientifically meaningful or appropriate shapes. In this paper, we focus on the regression spline technique and develop a method to help researchers select the most suitable shape to describe their data among increasing, decreasing, convex and concave shapes. Specifically, we develop a technique based on mixed effects regression spline to analyze hormonal data described in this paper. The proposed methodology is general enough to be applied to other similar problems. We illustrate the method using a prenatal screening program data set.

Keywords: Regression spline; Shape-restricted; Mixed effects model.; Primary: 62G10; 62F30; Secondary: 62P10. (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
http://link.springer.com/10.1007/s13571-020-00246-7 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sankhb:v:83:y:2021:i:1:d:10.1007_s13571-020-00246-7

Ordering information: This journal article can be ordered from
http://www.springer.com/statistics/journal/13571

DOI: 10.1007/s13571-020-00246-7

Access Statistics for this article

Sankhya B: The Indian Journal of Statistics is currently edited by Dipak Dey

More articles in Sankhya B: The Indian Journal of Statistics from Springer, Indian Statistical Institute
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().

 
Page updated 2021-05-29
Handle: RePEc:spr:sankhb:v:83:y:2021:i:1:d:10.1007_s13571-020-00246-7