RT Journal Article
T1 New statistics to test log-linear modeling hypothesis with no distributional specifications and clusters with homogeneous correlation
A1 Alonso Revenga, Juana María
A1 Martín Apaolaza, Nirian
A1 Pardo Llorente, Leandro
A2 Brugnano, Luigi
A2 Efendiev, Yalchin
A2 Keller, André
A2 Kwok-Po, Michael
A2 Romani, Lucia
A2 Tank. Fatih, 
AB 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.
PB Elsevier
SN 0377-0427
YR 2020
FD 2020-08-15
LK https://hdl.handle.net/20.500.14352/104517
UL https://hdl.handle.net/20.500.14352/104517
LA eng
NO 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.
NO Ministerio de Ciencia, Innovación y Universidades
DS Docta Complutense
RD 20 jul 2024