A generalized framework for modelling ordinal data

In several applied disciplines, as Economics, Marketing, Business, Sociology, Psychology, Political science, Environmental research and Medicine, it is common to collect data in the form of ordered categorical observations. In this paper, we introduce a class of models based on mixtures of discrete...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Statistical methods & applications Ročník 25; číslo 2; s. 163 - 189
Hlavní autoři: Iannario, Maria, Piccolo, Domenico
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2016
Springer Nature B.V
Témata:
Applied mathematics
Asymptotic methods
Asymptotic properties
Business
Case studies
Chemistry and Earth Sciences
Computer Science
Computer simulation
Consistency
Data analysis
Economics
Experiments
Finance
Generalized linear models
Health Sciences
Humanities
Insurance
Law
Management
Mathematics and Statistics
Maximum likelihood method
Medicine
Physics
Polls & surveys
Preferences
Probability
Random variables
Shelters
Simulation
Sociology
Statistical analysis
Statistical Theory and Methods
Statistics
Statistics for Business
Statistics for Engineering
Statistics for Life Sciences
Statistics for Social Sciences
models
choices
Rating survey
Ordinal data
ISSN:1618-2510, 1613-981X
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:In several applied disciplines, as Economics, Marketing, Business, Sociology, Psychology, Political science, Environmental research and Medicine, it is common to collect data in the form of ordered categorical observations. In this paper, we introduce a class of models based on mixtures of discrete random variables in order to specify a general framework for the statistical analysis of this kind of data. The structure of these models allows the interpretation of the final response as related to feeling, uncertainty and a possible shelter option and the expression of the relationship among these components and subjects’ covariates. Such a model may be effectively estimated by maximum likelihood methods leading to asymptotically efficient inference. We present a simulation experiment and discuss a real case study to check the consistency and the usefulness of the approach. Some final considerations conclude the paper.
Bibliografie:SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ObjectType-Article-1
ObjectType-Feature-2
content type line 23
ISSN:1618-2510
1613-981X
DOI:10.1007/s10260-015-0316-9