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Pairwise likelihood inference for multivariate ordinal responses with applications to customer satisfaction
Author(s) -
Kenne Pagui Euloge Clovis,
Canale Antonio
Publication year - 2015
Publication title -
applied stochastic models in business and industry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.413
H-Index - 40
eISSN - 1526-4025
pISSN - 1524-1904
DOI - 10.1002/asmb.2147
Subject(s) - categorical variable , pairwise comparison , latent variable , statistics , multivariate statistics , covariate , econometrics , inference , restricted maximum likelihood , estimator , ordered probit , mathematics , computer science , maximum likelihood , artificial intelligence
A common practice in customer satisfaction analysis is to administer surveys where subjects are asked to express opinions on a number of statements, or satisfaction scales, by use of ordered categorical responses. Motivated by this application, we propose a pseudo‐likelihood approach to estimate the dependence structure among multivariate categorical variables. As it is commonly carried out in this area, we assume that the responses are related to latent continuous variables that are truncated to induce categorical responses. A Gaussian likelihood is assumed for the latent variables leading to the so‐called ordered probit model. Because the calculation of the exact likelihood is computationally demanding, we adopt an approximate solution based on pairwise likelihood. To asses the performance of the approach, simulation studies are conducted comparing the proposed method with standard likelihood methods. A parametric bootstrap approach to evaluate the variance of the maximum pairwise likelihood estimator is proposed and discussed. An application to customer satisfaction survey is performed showing the effectiveness of the approach in the presence of covariates and under other generalizations of the model. Copyright © 2015 John Wiley & Sons, Ltd.