Regularization techniques in joinpoint regression

Joinpoint regression models are popular in various situations (modeling different trends in economics, mortality and incidence series or epidemiology studies and clinical trials). The literature on joinpoint regression mostly focuses on either the frequentist point of view, or discusses Bayesian app...

Full description

Saved in:
Bibliographic Details
Published in:Statistical papers (Berlin, Germany) Germany), 2016-12, Vol.57 (4), p.939-955
Main Authors: Maciak, Matúš, Mizera, Ivan
Format: Article
Language:English
Subjects:
Bayesian analysis
Clinical trials
Economic Theory/Quantitative Economics/Mathematical Methods
Economics
Epidemiology
Finance
Insurance
Management
Mathematical models
Mathematics and Statistics
Mortality
Operations Research/Decision Theory
Probability Theory and Stochastic Processes
Regression
Regression analysis
Regular Article
Regularization
Sparsity
Statistics
Statistics for Business
Studies
Texts
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Joinpoint regression models are popular in various situations (modeling different trends in economics, mortality and incidence series or epidemiology studies and clinical trials). The literature on joinpoint regression mostly focuses on either the frequentist point of view, or discusses Bayesian approaches instead. A model selection step in all these scenarios considers only some limited set of alternatives, from which the final model is chosen. We present a different model estimation approach: the final model is selected out of all possible alternatives admitted by the data. We apply the L 1 -regularization idea and via the sparsity principle we identify significant joinpoint locations to construct the final model. Some theoretical results and practical examples are given as well.
ISSN:0932-5026
1613-9798
DOI:10.1007/s00362-016-0823-2