A gradient-based deletion diagnostic measure for generalized linear mixed models

Marco Enea; Antonella Plaia; Marco Enea

A gradient-based deletion diagnostic measure for generalized linear mixed models

Risultato della ricerca: Article › peer review

Abstract

A gradient-statistic-based diagnostic measure is developed in the context of the generalized linear mixed models. Its performance is assessed by some real examples and simulation studies, in terms of ability in detecting influential data structures and of concordance with the most used influence measures.

Lingua originale	English
pagine (da-a)	1972-1982
Numero di pagine	11
Rivista	COMMUNICATIONS IN STATISTICS. THEORY AND METHODS
Volume	46
Stato di pubblicazione	Published - 2016

All Science Journal Classification (ASJC) codes

Statistics and Probability

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title = "A gradient-based deletion diagnostic measure for generalized linear mixed models",

abstract = "A gradient-statistic-based diagnostic measure is developed in the context of the generalized linear mixed models. Its performance is assessed by some real examples and simulation studies, in terms of ability in detecting influential data structures and of concordance with the most used influence measures.",

author = "Marco Enea and Antonella Plaia and Marco Enea",

year = "2016",

language = "English",

volume = "46",

pages = "1972--1982",

journal = "Communications in Statistics - Theory and Methods",

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T1 - A gradient-based deletion diagnostic measure for generalized linear mixed models

AU - Enea, Marco

AU - Plaia, Antonella

AU - Enea, Marco

PY - 2016

Y1 - 2016

N2 - A gradient-statistic-based diagnostic measure is developed in the context of the generalized linear mixed models. Its performance is assessed by some real examples and simulation studies, in terms of ability in detecting influential data structures and of concordance with the most used influence measures.

AB - A gradient-statistic-based diagnostic measure is developed in the context of the generalized linear mixed models. Its performance is assessed by some real examples and simulation studies, in terms of ability in detecting influential data structures and of concordance with the most used influence measures.

UR - http://hdl.handle.net/10447/180656

M3 - Article

VL - 46

SP - 1972

EP - 1982

JO - Communications in Statistics - Theory and Methods

JF - Communications in Statistics - Theory and Methods

SN - 0361-0926

ER -