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Titolo:
Modelling ordered categorical data: Recent advances and future challenges
Autore:
Agresti, A;
Indirizzi:
Univ Florida, Dept Stat, Gainesville, FL 32611 USA Univ Florida Gainesville FL USA 32611 ept Stat, Gainesville, FL 32611 USA
Titolo Testata:
STATISTICS IN MEDICINE
fascicolo: 17-18, volume: 18, anno: 1999,
pagine: 2191 - 2207
SICI:
0277-6715(19990930)18:17-18<2191:MOCDRA>2.0.ZU;2-0
Fonte:
ISI
Lingua:
ENG
Soggetto:
GENERALIZED ESTIMATING EQUATIONS; SPARSE CONTINGENCY-TABLES; LOG-LINEAR MODELS; ORDINAL LOGISTIC-REGRESSION; SAMPLE-SIZE CALCULATIONS; PROPORTIONAL ODDS MODEL; EXACT CONDITIONAL TESTS; LIKELIHOOD RATIO TESTS; PAIRED-COMPARISON DATA; LATENT CLASS ANALYSIS;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Clinical Medicine
Life Sciences
Citazioni:
155
Recensione:
Indirizzi per estratti:
Indirizzo: Agresti, A Univ Florida, Dept Stat, Gainesville, FL 32611 USA Univ Florida Gainesville FL USA 32611 ainesville, FL 32611 USA
Citazione:
A. Agresti, "Modelling ordered categorical data: Recent advances and future challenges", STAT MED, 18(17-18), 1999, pp. 2191-2207

Abstract

This article summarizes recent advances in the modelling of ordered categorical (ordinal) response variables. We begin by reviewing some models for ordinal data introduced in the literature in the past 25 years. We then survey recent extensions of these models and related methodology for special types of applications, such as for repeated measurement and other forms of clustering. We also survey other aspects of ordinal modelling, such as small-sample analyses, power and sample size considerations, and availability of software. Throughout, we suggest problem areas for future research and we highlight challenges for statisticians who deal with ordinal data. Copyright(C) 1999 John Wiley & Sons, Ltd.

ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 24/09/20 alle ore 05:05:43