Regularization techniques in joinpoint regression

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F16%3A10330046" target="_blank" >RIV/00216208:11320/16:10330046 - isvavai.cz</a>
Result on the web
<a href="http://link.springer.com/article/10.1007/s00362-016-0823-2?wt_mc=Internal.Event.1.SEM.ArticleAuthorOnlineFirst" target="_blank" >http://link.springer.com/article/10.1007/s00362-016-0823-2?wt_mc=Internal.Event.1.SEM.ArticleAuthorOnlineFirst</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s00362-016-0823-2" target="_blank" >10.1007/s00362-016-0823-2</a>

Result language
angličtina
Original language name
Regularization techniques in joinpoint regression
Original language description
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 L1L1-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.
Czech name
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Czech description
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Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
BA - General mathematics
OECD FORD branch
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Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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