Choosing the best Rukhin goodness-of-fit statistics
| dc.contributor.author | Marhuenda García, Yolanda | |
| dc.contributor.author | Morales González, Domingo | |
| dc.contributor.author | Pardo Llorente, Julio Ángel | |
| dc.contributor.author | Pardo Llorente, María del Carmen | |
| dc.date.accessioned | 2023-06-20T09:43:10Z | |
| dc.date.available | 2023-06-20T09:43:10Z | |
| dc.date.issued | 2005-06-01 | |
| dc.description.abstract | The testing for goodness-of-fit in multinomial sampling contexts is usually based on the asymptotic distribution of Pearson-type chi-squared statistics. However, approximations are not justified for those cases where sample size and number of cells permit the use of adequate algorithms to calculate the exact distribution of test statistics in a reasonable time. In particular, Rukhin statistics, containing chi(2) and Neyman's modified chi(2) statistics, are considered for testing uniformity. Their exact distributions are calculated for different sample sizes and number of cells. Several exact power comparisons are carried out to analyse the behaviour of selected statistics. As a result of the numerical study some recommendations are given. Conclusions may be extended to testing the goodness of fit to a given absolutely continuous cumulative distribution function. | |
| dc.description.department | Depto. de Estadística e Investigación Operativa | |
| dc.description.faculty | Fac. de Ciencias Matemáticas | |
| dc.description.refereed | TRUE | |
| dc.description.status | pub | |
| dc.eprint.id | https://eprints.ucm.es/id/eprint/17494 | |
| dc.identifier.doi | 10.1016/j.csda.2004.06.003 | |
| dc.identifier.issn | 0167-9473 | |
| dc.identifier.officialurl | http://www.sciencedirect.com/science/article/pii/S0167947304001756 | |
| dc.identifier.relatedurl | http://www.sciencedirect.com | |
| dc.identifier.uri | https://hdl.handle.net/20.500.14352/50242 | |
| dc.issue.number | 3 | |
| dc.journal.title | Computational Statistics and Data Analysis | |
| dc.language.iso | eng | |
| dc.page.final | 662 | |
| dc.page.initial | 643 | |
| dc.publisher | Elsevier Science | |
| dc.relation.projectID | BMF2003-00892 | |
| dc.relation.projectID | BMF2003-04820 | |
| dc.rights.accessRights | restricted access | |
| dc.subject.cdu | 519.22 | |
| dc.subject.keyword | Goodness-of-fit statistics | |
| dc.subject.keyword | Rukhin’s divergence | |
| dc.subject.keyword | Algorithms | |
| dc.subject.keyword | Exact distribution function | |
| dc.subject.keyword | Exact powers | |
| dc.subject.ucm | Estadística matemática (Matemáticas) | |
| dc.subject.unesco | 1209 Estadística | |
| dc.title | Choosing the best Rukhin goodness-of-fit statistics | |
| dc.type | journal article | |
| dc.volume.number | 49 | |
| dcterms.references | Ali, S.M., Silvey, S.D., 1966.A general class of coefficients of divergence of one distribution from another. J. Roy. Statist. Soc. B 26, 131–142. Cressie, N., Read, T.R.C., 1984. Multinomial goodness-of-fit tests. J. Roy. Statist. Soc. B 46, 440–464. Csiszár, I., 1963. Eine Informationstheoretische Ungleichung und ihre Anwendung auf den Beweis der Ergodizität on MarkhoffschenKetten. Publications of the Mathematical Institute of HungarianAcademy of Sciences, Series A, Vol. 8, pp. 85–108. Liese, F., Vajda, I., 1987. Convex Statistical Distances. Teubner, Leipzig. Marhuenda, M.A., Marhuenda, Y., Morales, D., 2003a. On the computation of the exact distribution of power divergence test statistics. Kybernetika 39 (1), 55–74. Marhuenda,Y.,Morales, D., Pardo, J.A., Pardo, M.C., 2003b. Exact distribution function for the Rukhin goodnessof-fit statistics. Technical report I-2003-13, Operation Research Center, Miguel Hernández University of Elche. Menéndez, M.L., Pardo, J.A., Pardo, L., Pardo, M.C., 1997. Asymptotic approximations for the distribution of the (h,)—divergence goodness-of-fit statistic: applications to Renyi’s statistics. Kybernetes 26 (4), 442–452. Pardo, J.A., Pardo, M.C., 1999. Small-sample comparisons for the Rukhin goodness-of-fit-statistics. Statist. Pap. 40, 159–174. Pearson, K., 1900. On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling. Philos.Mag. 50, 157–172. Rao, C.R., 1965. Linear Statistical Inference and its Applications.Wiley, NewYork. Read, T.R.C., Cressie, N.A.C., 1988. Goodness-of-fit Statistics for Discrete Multivariate Data. Springer,NewYork. Rukhin, A.L., 1994. Optimal estimatorforthe mixture parameterby the method of moments and information affinity. in: Transactions of the 12th Prague Conference on Information Theory.pp. 214–219. Vajda, I., 1989. Theory of Statistical Inference and Information. Kluwer Academic Publishers, Dordrecht. Zografos, K., Ferentinos, K., Papaioannou, T., 1990. phi-divergence statistics: sampling properties and multinomial goodness of fit and divergence tests. Commun. Statist.—Theory Methods 19 (5), 1785–1802. | |
| dspace.entity.type | Publication | |
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| relation.isAuthorOfPublication | 5e051d08-2974-4236-9c25-5e14369a7b61 | |
| relation.isAuthorOfPublication.latestForDiscovery | 4d5cedd9-975b-43fb-bc2e-f55dec36a2bf |
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