Modelling ordered categorical data: recent advances and future challenges.
作者： Alan Agresti
DOI: 10.1002/(SICI)1097-0258(19990915/30)18:17/18<2191::AID-SIM249>3.0.CO;2-M
关键词: Statistical model 、 Econometrics 、 Information interpretation 、 Computer aid 、 Sample size determination 、 Ordered set 、 Data science 、 Ordinal data 、 Cluster analysis 、 Mathematics 、 Categorical variable
摘要: This article summarizes recent advances in the modelling of ordered categorical (ordinal) response variables. We begin by reviewing some models for ordinal data introduced literature past 25 years. then survey extensions these and related methodology special types applications, such as repeated measurement other forms clustering. also aspects modelling, small-sample analyses, power sample size considerations, availability software. Throughout, we suggest problem areas future research highlight challenges statisticians who deal with data.
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