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Article: Factor analysis for paired ranked data with application on parent-child value orientation preference data

TitleFactor analysis for paired ranked data with application on parent-child value orientation preference data
Authors
Issue Date2013
Citation
Computational Statistics, 2013, v. 28 n. 5, p. 1915-1945 How to Cite?
AbstractRanking data appear in everyday life and arise in many fields of study such as marketing, psychology and politics. Very often, the key objective of analyzing and modeling ranking data is to identify underlying factors that affect the individuals’ choice behavior. Factor analysis for ranking data is one of the most widely used methods to tackle the aforementioned problem. Recently, Yu et al. [J R Stat Soc Ser A (Statistics in Society) 168:583–597, 2005] have developed factor models for ranked data in which each individual is asked to rank a set of items. However, paired ranked data may arisewhen the same set of items are ranked by a pair of judges such as a couple in a family. This paper extended the factor model to accommodate such paired ranked data. The Monte Carlo expectation-maximization algorithm was used for parameter estimation, at which the E-step is implemented via the Gibbs Sampler. For model assessment and selection, a tailor-made method called the bootstrap predictive checks approach was proposed. Simulation studies were conducted to illustrate the proposed estimation and model selection method. The proposed method was applied to analyze a parent–child partially ranked data collected from a value priorities survey carried out in the United States.
Persistent Identifierhttp://hdl.handle.net/10722/203427

 

DC FieldValueLanguage
dc.contributor.authorYu, PLHen_US
dc.contributor.authorLEE, Hen_US
dc.contributor.authorWAN, WMen_US
dc.date.accessioned2014-09-19T15:10:27Z-
dc.date.available2014-09-19T15:10:27Z-
dc.date.issued2013en_US
dc.identifier.citationComputational Statistics, 2013, v. 28 n. 5, p. 1915-1945en_US
dc.identifier.urihttp://hdl.handle.net/10722/203427-
dc.description.abstractRanking data appear in everyday life and arise in many fields of study such as marketing, psychology and politics. Very often, the key objective of analyzing and modeling ranking data is to identify underlying factors that affect the individuals’ choice behavior. Factor analysis for ranking data is one of the most widely used methods to tackle the aforementioned problem. Recently, Yu et al. [J R Stat Soc Ser A (Statistics in Society) 168:583–597, 2005] have developed factor models for ranked data in which each individual is asked to rank a set of items. However, paired ranked data may arisewhen the same set of items are ranked by a pair of judges such as a couple in a family. This paper extended the factor model to accommodate such paired ranked data. The Monte Carlo expectation-maximization algorithm was used for parameter estimation, at which the E-step is implemented via the Gibbs Sampler. For model assessment and selection, a tailor-made method called the bootstrap predictive checks approach was proposed. Simulation studies were conducted to illustrate the proposed estimation and model selection method. The proposed method was applied to analyze a parent–child partially ranked data collected from a value priorities survey carried out in the United States.en_US
dc.languageengen_US
dc.relation.ispartofComputational Statisticsen_US
dc.titleFactor analysis for paired ranked data with application on parent-child value orientation preference dataen_US
dc.typeArticleen_US
dc.identifier.emailYu, PLH: plhyu@hku.hken_US
dc.identifier.authorityYu, PLH=rp00835en_US
dc.identifier.hkuros237505en_US
dc.identifier.volume28en_US
dc.identifier.issue5en_US
dc.identifier.spage1915en_US
dc.identifier.epage1945en_US

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