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Influence analysis for the factor analysis model with ranking data.
Br J Math Stat Psychol. 2008 May; 61(Pt 1):133-61.BJ

Abstract

Influence analysis is an important component of data analysis, and the local influence approach has been widely applied to many statistical models to identify influential observations and assess minor model perturbations since the pioneering work of Cook (1986). The approach is often adopted to develop influence analysis procedures for factor analysis models with ranking data. However, as this well-known approach is based on the observed data likelihood, which involves multidimensional integrals, directly applying it to develop influence analysis procedures for the factor analysis models with ranking data is difficult. To address this difficulty, a Monte Carlo expectation and maximization algorithm (MCEM) is used to obtain the maximum-likelihood estimate of the model parameters, and measures for influence analysis on the basis of the conditional expectation of the complete data log likelihood at the E-step of the MCEM algorithm are then obtained. Very little additional computation is needed to compute the influence measures, because it is possible to make use of the by-products of the estimation procedure. Influence measures that are based on several typical perturbation schemes are discussed in detail, and the proposed method is illustrated with two real examples and an artificial example.

Authors+Show Affiliations

Department of Mathematics, South-east University, Nanjing, China.No affiliation info availableNo affiliation info available

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

18482479

Citation

Xu, Liang, et al. "Influence Analysis for the Factor Analysis Model With Ranking Data." The British Journal of Mathematical and Statistical Psychology, vol. 61, no. Pt 1, 2008, pp. 133-61.
Xu L, Poon WY, Lee SY. Influence analysis for the factor analysis model with ranking data. Br J Math Stat Psychol. 2008;61(Pt 1):133-61.
Xu, L., Poon, W. Y., & Lee, S. Y. (2008). Influence analysis for the factor analysis model with ranking data. The British Journal of Mathematical and Statistical Psychology, 61(Pt 1), 133-61. https://doi.org/10.1348/000711006X169991
Xu L, Poon WY, Lee SY. Influence Analysis for the Factor Analysis Model With Ranking Data. Br J Math Stat Psychol. 2008;61(Pt 1):133-61. PubMed PMID: 18482479.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Influence analysis for the factor analysis model with ranking data. AU - Xu,Liang, AU - Poon,Wai-Yin, AU - Lee,Sik-Yum, PY - 2008/5/17/pubmed PY - 2008/8/23/medline PY - 2008/5/17/entrez SP - 133 EP - 61 JF - The British journal of mathematical and statistical psychology JO - Br J Math Stat Psychol VL - 61 IS - Pt 1 N2 - Influence analysis is an important component of data analysis, and the local influence approach has been widely applied to many statistical models to identify influential observations and assess minor model perturbations since the pioneering work of Cook (1986). The approach is often adopted to develop influence analysis procedures for factor analysis models with ranking data. However, as this well-known approach is based on the observed data likelihood, which involves multidimensional integrals, directly applying it to develop influence analysis procedures for the factor analysis models with ranking data is difficult. To address this difficulty, a Monte Carlo expectation and maximization algorithm (MCEM) is used to obtain the maximum-likelihood estimate of the model parameters, and measures for influence analysis on the basis of the conditional expectation of the complete data log likelihood at the E-step of the MCEM algorithm are then obtained. Very little additional computation is needed to compute the influence measures, because it is possible to make use of the by-products of the estimation procedure. Influence measures that are based on several typical perturbation schemes are discussed in detail, and the proposed method is illustrated with two real examples and an artificial example. SN - 0007-1102 UR - https://www.unboundmedicine.com/medline/citation/18482479/Influence_analysis_for_the_factor_analysis_model_with_ranking_data_ L2 - https://doi.org/10.1348/000711006X169991 DB - PRIME DP - Unbound Medicine ER -