The Annals of Statistics

A likelihood ratio test for $MTP_2$ within binary variables

Francesco Bartolucci and Antonio Forcina

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Multivariate Totally Positive $(MTP_2)$ binary distributions have been studied in many fields, such as statistical mechanics, computer storage and latent variable models. We show that $MTP_2$ is equivalent to the requirement that the parameters of a saturated log-linear model belong to a convex cone, and we provide a Fisher-scoring algorithm for maximum likelihood estimation.We also show that the asymptotic distribution of the log-likelihood ratio is a mixture of chi-squares (a distribution known as chi-bar-squared in the literature on order restricted inference); for this we derive tight bounds which turn out to have very simple forms. A potential application of this method is for Item Response Theory (IRT) models, which are used in educational assessment to analyse the responses of a group of subjects to a collection of questions (items): an important issue within IRT is whether the joint distribution of the manifest variables is compatible with a single latent variable representation satisfying local independence and monotonicity which, in turn, imply that the joint distribution of item responses is $MTP_2$.

Article information

Ann. Statist., Volume 28, Number 4 (2000), 1206-1218.

First available in Project Euclid: 12 March 2002

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Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier

Primary: 62H20: Measures of association (correlation, canonical correlation, etc.)
Secondary: 62G10: Hypothesis testing 62H15: Hypothesis testing 62H17: Contingency tables

Chi-bar-squared distribution conditional association item response models order-restricted inference stochastic ordering


Bartolucci, Francesco; Forcina, Antonio. A likelihood ratio test for $MTP_2$ within binary variables. Ann. Statist. 28 (2000), no. 4, 1206--1218. doi:10.1214/aos/1015956713.

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