A multiple-record systems estimation method that takes observed and unobserved heterogeneity into account

Biometrics

Volumn 60, Issue 2, 2004, Pages 510-516

  Stanghellini, Elena ^a   Van Der Heijden, Peter G M ^b  

^a UNIVERSITY OF PERUGIA   (Italy)

^b UTRECHT UNIVERSITY   (Netherlands)

Author keywords

Conditional independence model; Extended latent class model; Graphical models; Hierarchical log linear models; Identifiability; Observed information matrix; Profile log likelihood; Strata

Indexed keywords

HIERARCHICAL SYSTEMS;

CONDITIONAL INDEPENDENCE MODEL; CONDITIONAL INDEPENDENCES; EXTENDED LATENT CLASS MODEL; GRAPHICAL MODEL; HIERARCHICAL LOG LINEAR MODEL; IDENTIFIABILITY; INDEPENDENCE MODELS; INFORMATION MATRIX; LATENT CLASS MODEL; LOG LIKELIHOOD; LOGLINEAR MODEL; OBSERVED INFORMATION MATRIX; PROFILE LOG LIKELIHOOD; STRATUM;

REGRESSION ANALYSIS;

ESTIMATION METHOD; HETEROGENEITY; POPULATION ESTIMATION; POPULATION SIZE;

ARTICLE; ASSAY; BIOMETRY; CONFIDENCE INTERVAL; COVARIANCE; EPIDEMIOLOGICAL DATA; INDEPENDENCE; MATHEMATICAL ANALYSIS; MATHEMATICAL MODEL; MATHEMATICAL PARAMETERS; POPULATION; RECORDING; STATISTICAL MODEL;

EID: 2942688002     PISSN: 0006341X     EISSN: None     Source Type: Journal    
DOI: 10.1111/j.0006-341X.2004.00197.x     Document Type: Article

References (23)

1. Agresti, A. (1994). Simple capture-recapture models permitting unequal catchability and variable sampling effort. Biometrics 50, 494-500.
2. Agresti, A. and Lang, J. B. (1993). Quasi-symmetric latent class models, with application to rater agreement. Biometrics 49, 131-139.
3. Alho, J. M. (1990). Logistic regression in capture-recapture models. Biometrics 46, 623-635.
4. Bartolucci, F. and Forcina, A. (2001). Analysis of capture-recapture data with a Rasch-type model allowing for conditional dependence and multidimensionality. Biometrics 57, 714-719.
5. Biggeri, A., Stanghellini, E., Merletti, F., and Marchi, M. (1999). Latent class models for varying catchability and correlation among sources in capture-recapture estimation of the size of a human population. Statistica Applicata 11, 563-576.
6. Bishop, Y. M. M., Fienberg, S. E., and Holland, P. W. (1975). Discrete Multivariate Analysis. Cambridge, Massachusetts: MIT Press.
7. Bruno, G., Biggeri, A., LaPorte, R. E., McCarty, D., Merletti, F., and Pagano, G. (1994). Appplication of capture-recapture to count diabetes? Diabetes Care 17, 548-556.
8. Catchpole, E. and Morgan, B. J. T. (1997). Detecting parameter redundancy. Biometrika 84, 187-196.
9. Chao, A., Tsay, P. K., Lin, S.-H., Shau, W.-Y., and Chao, D. Y. (2001). The application of capture-recapture models to epidemiological data. Statistics in Medicine 20, 3123-3157.
10. Cormack, R. M. (1992). Interval estimation for mark-recapture studies of closed populations. Biometrics 48, 567-576.
11. Coull, B. A. and Agresti, A. (1999). The use of mixed models to reflect heterogeneity in capture-recapture studies. Biometrics 55, 294-301.
12. Darroch, J. N., Fienberg, S. E., Glonek, G. F. V., and Junker, B.W. (1993). A three-sample multiple-recapture approach to census population estimation with heterogeneous catchability. Journal of the American Statistical Association 88, 1137-1148.
13. Edwards, D. (2000). Introduction to Graphical Modeling, 2nd edition. New York: Springer.
14. Farrington, C. P. (2002). Interval estimation for Poisson capture-recapture models in epidemiology. Statistics in Medicine 21, 3079-3092.
15. Fienberg, S. E., Johnson, M. S., and Junker, B. W. (1999). Classical multilevel and Bayesian approaches to population size estimation using multiple lists. Journal of the Royal Statistical Society, Series A 162, 383-405.
16. Goodman, L. A. (1974). Exploratory latent structure analysis using both identifiable and unidentifiable models. Biometrika 61, 215-231.
17. Huggins, R. M. (1991). Some practical aspects of a conditional likelihood approach to capture experiments. Biometrics 47, 725-732.
18. International Working Group for Disease Monitoring and Forecasting. (1995). Capture-recapture and multiple-record systems estimation 1: History and theoretical development. American Journal of Epidemiology 142, 1047-1058.
19. Lang, J. B. (1992). Obtaining the observed information matrix for the Poisson log linear model with incomplete data. Biometrika 79, 833-836.
20. Lindsay, B., Clogg, C., and Greco, J. (1991). Semiparametric estimation in the Rasch model and related exponential response models, including a simple latent class model for item analysis. Journal of the American Statistical Association 86, 96-107.
21. Louis, T. A. (1982). Finding the observed information matrix when using the EM algorithm. Journal of the Royal Statistical Society, Series B 44, 226-233.
22. Rothenberg, T. (1971). Identification in parametric model. Econometrica 39, 577-591.
23. Whittaker, J. (1990). Graphical Models in Applied Multivariate Statistics. New York: Wiley.