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dc.contributor.authorTapia, Alejandra
dc.contributor.authorLeiva, Víctor
dc.contributor.authorGalea, Manuel
dc.contributor.authorWerneck, Rachel
dc.description.abstractThis article proposes a methodology for diagnostics in a logistic regression with random intercept motivated by a biological study. The methodology includes local and global influence techniques allowing us to contrast the results of both types of influence. The proposed methodology is applied to a case study with real data to show its potential. This study corresponds to the reproduction of arachnids reporting how the local and global influence of atypical observations can modify the significance of parameters, and then the biological conclusions. The model fitting is evaluated through predictive indicators. The methodology is summarized in an algorithm and a demo example is implemented in R code to facilitate its application. To evaluate the performance of the methodology, Monte Carlo simulations are conducted.es_CL
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 Chile*
dc.sourceJournal of Statistical Computation and Simulation, 90(13), 2354-2383es_CL
dc.subjectCorrelated binary dataes_CL
dc.subjectMetropolis–hasting algorithmes_CL
dc.subjectMonte Carlo integration and simulationes_CL
dc.subjectR softwarees_CL
dc.subjectReproductive biologyes_CL
dc.titleOn a logistic regression model with random intercept: diagnostic analytics, simulation and biological applicationes_CL
dc.ucm.facultadFacultad de Ciencias Básicases_CL

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Atribución-NoComercial-SinDerivadas 3.0 Chile
Except where otherwise noted, this item's license is described as Atribución-NoComercial-SinDerivadas 3.0 Chile