Epitope profiling via mixture modeling of ranked data. (5th June 2014)

• Record Type: Journal Article
• Authors: Mollica, Cristina; Tardella, Luca
• Is Part Of: Statistics in medicine. Volume 33:Number 21(2014)
• Page Start: 3738
• Page End: 3758
• Publication Date: 2014-06-05
• Subjects: Medical statistics -- Periodicals; Statistique médicale -- Périodiques; Statistiques médicales -- Périodiques; 610.727
• DOI: 10.1002/sim.6224
• Languages: English
• ISSNs: 0277-6715

Abstract

<abstract abstract-type="main" id="sim6224-abs-0001"> <title> <x xml:space="preserve">Abstract</x> </title> <p id="sim6224-para-0001">We propose the use of probability models for ranked data as a useful alternative to a quantitative data analysis to investigate the outcome of bioassay experiments when the preliminary choice of an appropriate normalization method for the raw numerical responses is difficult or subject to criticism. We review standard distance‐based and multistage ranking models and propose an original generalization of the <italic>Plackett–Luce model</italic> to account for the order of the ranking elicitation process. The usefulness of the novel model is illustrated with its maximum likelihood estimation for a real data set. Specifically, we address the heterogeneous nature of the experimental units via model‐based clustering and detail the necessary steps for a successful likelihood maximization through a hybrid version of the expectation–maximization algorithm. The performance of the mixture model using the new distribution as mixture components is then compared with alternative mixture models for random rankings. A discussion on the interpretation of the identified clusters and a comparison with more standard quantitative approaches are finally provided. Copyright © 2014 John Wiley &amp; Sons, Ltd.</p> </abstract>