A penalized latent class model for ordinal data

Biostatistics

Volumn 9, Issue 2, 2008, Pages 249-262

  Desantis, Stacia M ^a   Houseman, E Andrés ^a   Coull, Brent A ^a   Stemmer Rachamimov, Anat ^b   Betensky, Rebecca A ^a  

^a HARVARD UNIVERSITY   (United States)

^b MASSACHUSETTS GENERAL HOSPITAL   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; BIOMETRY; CLASSIFICATION; CLUSTER ANALYSIS; DIAGNOSTIC PROCEDURE; HISTOLOGY; HUMAN; METHODOLOGY; NEURILEMOMA; PATHOLOGY; STANDARD; STATISTICAL MODEL; STATISTICS;

BIOMETRY; CLUSTER ANALYSIS; DIAGNOSTIC TECHNIQUES AND PROCEDURES; HISTOLOGICAL TECHNIQUES; HUMANS; LIKELIHOOD FUNCTIONS; NEURILEMMOMA; RESEARCH DESIGN; WEIGHTS AND MEASURES;

EID: 41149173077     PISSN: 14654644     EISSN: 14684357     Source Type: Journal    
DOI: 10.1093/biostatistics/kxm026     Document Type: Article

References (23)

1. AGRESTI, A. AND LANG, J. (1993). Quasi-symmetric latent class models, with application to rater agreement. Biometrics 49, 131-139.
2. BANDEEN-ROCHE, K., MIGLIORETTI, D. L., ZEGER, S. L AND RATHOUZ, P. J. (1997). Latent variable regression for multiple discrete outcomes. Journal of the American Statistical Association 92, 1375-1386.
3. BARTHOLOMEW, D. J. AND KNOTT, M. (1999). Latent Variable Models and Factor Analysis. London: Arnold.
4. BASER, M. E., FRIEDMAN, J. M. AND EVANS, D. G. R. (2006). Increasing the specificity of diagnostic criteria for schwannomatosis. Neurology 66, 730-732.
5. BURNHAM, K. P. AND ANDERSON, D. R. (2004). Multimodel inference: understanding AIC and BIC in model selection. Sociological Methods Research 33, 261-304.
6. CHENG, S. C., WEI, L. J. AND YING, Z. (1995). Analysis of transformation models with censored data. Biometrika 82, 835-845.
7. CROON, M. (1990). Latent class analysis with ordered latent classes. British Journal of Mathematical and Statistical Psychology 43, 171-192.
8. DAYTON, C. M. AND MACREADY, G. B. (1988). Concomitant-variable latent-class models. Journal of the American Statistical Association 83, 173-178.
9. DEMPSTER, C. M., LAIRD, N. M. AND RUBIN, D. B. (1977). Maximum likelihood from incomplete data via EM algorithm. Journal of the Royal Statistical Society, Series B 39, 1-38.
10. GOODMAN, L. A. (1974). Exploratory latent structure-analysis using both identifiable and unidentifiable models. Biometrika 61, 215-231.
11. HOIJTINK, H. (1998). Constrained latent class analysis using the Gibbs sampler and posterior predictive p-values: applications to educational testing. Statistica Sinica 8, 691-711.
12. HOUSEMAN, E. A., COULL, B. A. AND BETENSKY, R. A. (2006). Feature-specific constrained latent class analysis for genomic data. Biometrics 62, 1062-1070.
13. HUANG, G. H. AND BANDEEN-ROCHE, K. (2004). Building an identifiable latent class model with covariate effects on underlying and measured variables. Psychometrika 69, 5-32.
14. LARSEN, K. (2004). Joint analysis of time-to-event and multiple binary indicators of latent classes. Biometrics 60, 85-92.
15. LAZARSFELD, P. F. (1950). Measurement and Prediction. Princeton, NJ: Princeton University Press.
16. LAZARSFELD, P. F AND HENRY, N. W. (1968). Latent Structure Analysis. New York: Houghton-Mifflin.
17. LIN, T. H. AND DAYTON, C. M. (1997). Model selection information criteria for non-nested latent class models. Journal of Educational and Behavioral Statistics 22, 249-264.
18. MACCOLLIN, M., CHIOCCA, E. A., EVANS, D. G., FRIEDMAN, J. M., HORVITZ, R., JARAMILLO, D., LEV, M., MAUTNER, V. F., NIIMURA, M., PLOTKIN, S. R. and others (2005). Diagnostic criteria for schwannomatosis. Neurology 64, 1838-1845.
19. MCHUGH, T. B. (1956). Efficient estimation and local identification in latent class analysis. Psychometrika 21, 331-347.
20. MEULDERS, M., BOECK, P. D., KUPPENS, P. AND MECHELEN, I. V. (2002). Constrained latent class analysis of three-way three-mode data. Journal of Classification 19, 277-302.
21. MOOIJAART, A. AND VAN DER HEIJDEN, P. G. M. (1992). The EM algorithm for latent class analysis with equality constraints. Psychometrika 52, 261-269.
22. MOUSTAKI, I. AND PAPAGEORGIOU, I. (2005). Latent class models for mixed variables with applications in Archaeometry. Computational Statistics and Data Analysis 48, 659-675.
23. POLIANI, P. L., DESANTIS, S., BETENSKY, R. A., HURWITZ, E., NUTT, C., MACCOLLIN, M. AND STEMMER-RACHAMIMOV, A. O. (2006). Clinicopathological correlates of schwannomas: defining the pathological features of sporadic, nf2-associated and schwannomatosis-associated schwannomas. Journal of Neuropathology and Experimental Neurology (submitted).