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Epitope profiling via mixture modeling of ranked data
Author(s) -
Mollica Cristina,
Tardella Luca
Publication year - 2014
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.6224
Subject(s) - computer science , normalization (sociology) , expectation–maximization algorithm , mixture model , ranking (information retrieval) , cluster analysis , profiling (computer programming) , generalization , maximization , raw data , data mining , data set , maximum likelihood , statistics , artificial intelligence , mathematics , mathematical optimization , mathematical analysis , sociology , anthropology , programming language , operating system
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 Plackett–Luce model 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 & Sons, Ltd.