Minimum phi-divergence estimator in logistic regression models
| dc.contributor.author | Pardo Llorente, Julio Ángel | |
| dc.contributor.author | Pardo Llorente, Leandro | |
| dc.contributor.author | Pardo Llorente, María del Carmen | |
| dc.date.accessioned | 2023-06-20T09:43:09Z | |
| dc.date.available | 2023-06-20T09:43:09Z | |
| dc.date.issued | 2006-01 | |
| dc.description.abstract | A general class of minimum distance estimators for logistic regression models based on the phi- divergence measures is introduced: The minimum phi- divergence estimator, which is seen to be a generalization of the maximum likelihood estimator. Its asymptotic properties are studied as well as its behaviour in small samples through a simulation study. | |
| dc.description.department | Depto. de Estadística e Investigación Operativa | |
| dc.description.faculty | Fac. de Ciencias Matemáticas | |
| dc.description.refereed | TRUE | |
| dc.description.sponsorship | DGI | |
| dc.description.status | pub | |
| dc.eprint.id | https://eprints.ucm.es/id/eprint/17492 | |
| dc.identifier.doi | 10.1007/s00362-005-0274-7 | |
| dc.identifier.issn | 0932-5026 | |
| dc.identifier.officialurl | http://www.springerlink.com/content/hv6733q0308v37gn/fulltext.pdf | |
| dc.identifier.relatedurl | http://www.springerlink.com | |
| dc.identifier.uri | https://hdl.handle.net/20.500.14352/50241 | |
| dc.issue.number | 1 | |
| dc.journal.title | Statistical Papers | |
| dc.language.iso | eng | |
| dc.page.final | 108 | |
| dc.page.initial | 91 | |
| dc.publisher | Springer Verlag | |
| dc.relation.projectID | BMF2003-00892 | |
| dc.rights.accessRights | restricted access | |
| dc.subject.cdu | 519.22 | |
| dc.subject.keyword | Logistic regression model | |
| dc.subject.keyword | Minimum C-divergence estimator | |
| dc.subject.keyword | Monte Carlo simulation | |
| dc.subject.keyword | Newton-Raphson method. | |
| dc.subject.ucm | Estadística matemática (Matemáticas) | |
| dc.subject.unesco | 1209 Estadística | |
| dc.title | Minimum phi-divergence estimator in logistic regression models | |
| dc.type | journal article | |
| dc.volume.number | 47 | |
| dcterms.references | Agresti, A. (1990). Categorical Data Analysis. John Wiley & Sons, New York. All, S. M. and Silvey, S. D. (1966). A general class of coefficients of divergence of one distribution from another. Journal of the Royal Statistical Society, Series B, 26, 131-142 Amemiya, T. (1972). Bivariate Probit Analysis: Minimum Chi-square Methods. Journal of American Statistical Association, 69, 940-944. Amemiya, T. (1981). Qualitative Response Models: A Survey. Journal of Economic Literature, XIX, 1483-1536. Amemiya, T. (1985). Advanced Econometrics, Basil Blackwell. Oxford and New York. Andersen, E.B. (1990). The Statistical Analysis of Categorical Data. Heidelberg: Springer-Verlag. Andersen, E.B. (1996). Introduction to the Statistical Analysis of Categorical Data. Springer-Verlag. Berkson, J. (1944). Application of logistic functions to bioassay. Journal of American Statistical Association, 39, 357-365. Berkson, J. (1953). A statistical precise and relatively simple method of estimating the bioassay with quantal response, based on the logistic function. Journal of American Statistical Association, 48, 565-599. Berkson, J. (1955). Maximum likelihood and minimum X 2 estimates of the logistic function. Journal of American Statistical Association, 50, 130-162. Cox, C. (1984). An elementary introduction to maximum likelihood estimation for multinomial models: Birch's theorem and delta method. The American Statistician, 38, 283-287. Cox, D.R. and Snell, E.J. (1989). Analysis of Binary Data. Chapman and Hall. Cressie, N. and Read, T. R. C. (1984). Multinomial goodness-of-fit tests. Journal of the Royal Statistical Society, Series B, 46~ 440-464. Cressie, N. and Pardo, L. (2000). Minimum C-divergence estimator and hierarchical testing in Loglinear models. Statistica Sinica, 10(3), 8674884. Cressie, N., Paxdo, L. and Pardo, M.C. (2003). Size and power considerations for testing Loglinear models using C-divergence test statistics. Statistica Sinica, 13(2),555-570. Csisz~, I. (1967). Information type measures of difference of probability distributions and indirect observations. Studia Scientiarum Mathematicarum Hungarica, 2~ 105-113. Flemmning, W. (1977). Functions of Several Variables, Second Edition, Springer-Verlag. New York. Hosmer, D. W. and Lemeshow, S. (1989). Applied Logistic Regression. New York, John Wiley. Jennings, D.E. (1986). Judging inference adequacy in Logistic regression. Journal of the American Statistical Association, 81(396), 987-990. Kullback, S. (1985). Kullback information. In Encyclopedia of Statistical Sciences, Volume 4 (editors S. Kotz and N. L. Johnson), 421-425. New York, John Wiley. Morales, D., Pardo, L. and Vajda, I. (1995). Asymptotic divergence of estimates of discrete distributions. Journal of Statistical Planning and Inference, 48, 347-369. Pardo, L. (1997). Statistical Information Theory. (In Spanish). Hesperides. Spain. Pardo, L. and Pardo, M.C. (2003). Minimum power-divergence in three-way contingency tables. Journal of Statistical Computation and Simulation, 73(11), 819-831. Pardo, M.C. and Pardo, J.A. (1999). Small-sample comparisons for the Rukhin goodness-of-fit statistics. Statistical Papers, 40(2), 159-174. Parr, W.C. (1981). Minimum distance estimation: a bibliography. Communication in Statistics (Theory and Methods), 12, 1205-1224. Rukhin. A.L. (1994). Optimal estimator for the mixture parameter by the method of moments and information affinity. Trans. l~th Prague Conference on Information Theory, 214-219. Wolfowitz, J. (1953). Estimation by the minimum distance method. Annals of the Institute of Statistical Mathematics, 5, 9-23. | |
| dspace.entity.type | Publication | |
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| relation.isAuthorOfPublication | a6409cba-03ce-4c3b-af08-e673b7b2bf58 | |
| relation.isAuthorOfPublication.latestForDiscovery | 5e051d08-2974-4236-9c25-5e14369a7b61 |
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