Semiparametric models encompass a huge family of models that have a fully parametric (finite number of parameters) with a nonparametric part. In general, the parametric part will be linear, and the semiparametric part will be treated as nuisance; but this is not always the case. One example where a semiparametric model would be relevant, could be for example modeling the ice-cream sales in terms of the weather and the price. It's likely that the sales-weather relationship is highly nonlinear (sales are really high when the temperature is high, but low when the temperature is moderate), whereas the price-sales one could be quite linear. In that case, we would want to treat the price effect as linear and the rest as nuisance.
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You're reading from R Statistics Cookbook
Product typeBook
Published inMar 2019
Reading LevelExpert
PublisherPackt
ISBN-139781789802566
Edition1st Edition
Languages
Tools
Concepts
Author (1)
Francisco Juretig
Francisco Juretig
Francisco Juretig has worked for over a decade in a variety of industries such as retail, gambling and finance deploying data-science solutions. He has written several R packages, and is a frequent contributor to the open source community.
Read more about Francisco Juretig
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R Statistics CookbookPublished in: Mar 2019Publisher: PacktISBN-13: 9781789802566
© 2019 Packt Publishing Limited All Rights Reserved
Author (1)
Francisco Juretig
Francisco Juretig has worked for over a decade in a variety of industries such as retail, gambling and finance deploying data-science solutions. He has written several R packages, and is a frequent contributor to the open source community.
Read more about Francisco Juretig