Artículo
Farall, A.; Maronna, R.; Tetzlaff, T. "A mixture model for the detection of Neosporosis without a gold standard" (2011) Journal of Applied Statistics. 38(5):913-926
Consulte la política de Acceso Abierto del editor
Abstract:
Neosporosis is a bovine disease caused by the parasite Neospora caninum. It is not yet sufficiently studied, and it is supposed to cause an important number of abortions. Its clinical symptoms do not yet allow the reliable identification of infected animals. Its study and treatment would improve if a test based on antibody counts were available. Knowing the distribution functions of observed counts of uninfected and infected cows would allow the determination of a cutoff value. These distributions cannot be estimated directly. This paper deals with the indirect estimation of these distributions based on a data set consisting of the antibody counts for some 200 pairs of cows and their calves. The desired distributions are estimated through a mixture model based on simple assumptions that describe the relationship between each cow and its calf. The model then allows the estimation of the cutoff value and of the error probabilities. © 2011 Taylor & Francis.
Registro:
| Documento: | Artículo |
| Título: | A mixture model for the detection of Neosporosis without a gold standard |
| Autor: | Farall, A.; Maronna, R.; Tetzlaff, T. |
| Filiación: | Alma Mater Studiorum, Università di Bologna, Rodríguez Peña 1464, Buenos Aires, Argentina Facultad de Ciencias Exactas, Universidad de La Plata, C.C. 172, La Plata 1900, Argentina C.I.C.P.B.A, La Plata, Argentina Departamento de Computación, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, 1428 Buenos Aires, Argentina |
| Palabras clave: | Bivariate mixtures; Density ratio model; Neosporosis |
| Año: | 2011 |
| Volumen: | 38 |
| Número: | 5 |
| Página de inicio: | 913 |
| Página de fin: | 926 |
| DOI: | http://dx.doi.org/10.1080/02664761003692381 |
| Título revista: | Journal of Applied Statistics |
| Título revista abreviado: | J. Appl. Stat. |
| ISSN: | 02664763 |
| Registro: | https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_02664763_v38_n5_p913_Farall |
Referencias:
- Anderson, J.A., Separate sample logistic discrimination (1972) Biometrika, 59, pp. 19-35
- Choi, Y.K., Johnson, W.O., Gardner, I.A., Collins, M., Bayesian estimation of ROC curves in the absence of a gold standard (2006) J. Agric. Biol. Environ. Stat., 11, pp. 210-229
- Cruz-Medina, I.R., Hettmansperger, T.P., Thomas, H., Semiparametric mixture models and repeated measures: The multinomial cut point model (2004) Appl. Stat., 53, pp. 463-474
- Davison, A.C., Hinkley, D.V., (1997) Bootstrap Methods and their Application, , Cambridge University Press, Cambridge, UK
- Dubey, J.P., Review of Neospora caninum and neosporosis in animals (2003) Korean J. Parasitol., 41, pp. 1-16
- Dubey, J.P., Schares, G., Review of diagnosis of bovine neosporosis (2006) Vet. Parasitol., 140, pp. 1-34
- French, N.P., Clancy, D., Davison, H.C., Trees, A.J., Mathe-matical models of Neospora caninum infection in dairy cattle: Transmission and options for control (1999) Int. J. Parasitol., 29, pp. 1691-1704
- Hall, P., Zhou, X., Nonparametric estimation of component distributions in a multivariate mixture (2003) Ann. Stat., 31, pp. 201-224
- Hall, P., Pakyari, R., Elmore, R., Nonparametric inference in multivariate mixtures (2005) Biometrika, 92, pp. 667-678
- Parr, W.C., Schucany, W.R., Minimum distance and robust estimation (1980) J. Am. Statist. Assoc., 75, pp. 616-624
- Qin, J., Empirical likelihood ratio based confidence intervals for mixture proportions (1999) Ann. Stat., 27, pp. 1368-13684
- Shao, J., Consistency of least-squares estimator and its jackknife variance estimator in non-linear models (1992) Can. J. Stat., 20, pp. 415-428
- Wang, C., Turnbull, B.W., Gröhn, Y.T., Nielsen, S.S., Estimating ROC curves with covariates when there is no perfect reference test for diagnosis of Johne's disease (2006) J. Dairy Sci., 89, pp. 3038-3046
- Wang, C., Turnbull, B.W., Gröhn, Y.T., Nielsen, S.S., Nonparametric estimation of ROC curves based on Bayesian models when the true disease state is unknown (2007) J. Agric. Biol. Environ. Stat., 12, pp. 128-146
- Zhou, X., Castelluccio, P., Zhou, C., Nonparametric estimation of ROC curves in the absence of a gold standard (2005) Biometrics, 61, pp. 600-609
- Zou, F., Fine, J.P., Yandell, B.S., On empirical likelihood for a semiparametric mixture model (2002) Biometrika, 89, pp. 61-75
Citas:
---------- APA ----------
Farall, A., Maronna, R. & Tetzlaff, T. (2011) . A mixture model for the detection of Neosporosis without a gold standard. Journal of Applied Statistics, 38(5), 913-926.http://dx.doi.org/10.1080/02664761003692381
---------- CHICAGO ----------
Farall, A., Maronna, R., Tetzlaff, T. "A mixture model for the detection of Neosporosis without a gold standard" . Journal of Applied Statistics 38, no. 5 (2011) : 913-926.http://dx.doi.org/10.1080/02664761003692381
---------- MLA ----------
Farall, A., Maronna, R., Tetzlaff, T. "A mixture model for the detection of Neosporosis without a gold standard" . Journal of Applied Statistics, vol. 38, no. 5, 2011, pp. 913-926.http://dx.doi.org/10.1080/02664761003692381
---------- VANCOUVER ----------
Farall, A., Maronna, R., Tetzlaff, T. A mixture model for the detection of Neosporosis without a gold standard. J. Appl. Stat. 2011;38(5):913-926.http://dx.doi.org/10.1080/02664761003692381
