New statistics to test log-linear modeling hypothesis with no distributional specifications and clusters with homogeneous correlation

Alonso Revenga, Juana María; Martín Apaolaza, Nirian; Pardo Llorente, Leandro

New statistics to test log-linear modeling hypothesis with no distributional specifications and clusters with homogeneous correlation

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Official URL

https://doi.org/10.1016/j.cam.2020.112757

Publication date

2020

Authors

Alonso Revenga, Juana María

Martín Apaolaza, Nirian

Pardo Llorente, Leandro

Editors

Publisher

Elsevier

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URI

https://hdl.handle.net/20.500.14352/104517

Citation

Alonso-Revenga, Martín y Pardo (2020) «New statistics to test log-linear modeling hypothesis with no distributional specifications and clusters with homogeneous correlation», Journal of Computational and Applied Mathematics, 374.

Abstract

Traditionally, the Dirichlet-multinomial distribution has been recognized as a key model for contingency tables generated by cluster sampling schemes. There are, however, other possible distributions appropriate for these contingency tables. This paper introduces new statistics capable of testing log-linear modeling hypotheses with distributional unspecification, when the individuals of the clusters are possibly homogeneously correlated. An estimator for the intracluster correlation coefficient, valid for different cluster sizes, plays a crucial role in the construction of the goodness-of-fit test-statistics.