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On some pre-test and Stein-rule phi-divergence test estimators in the independence model of categorical data

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2008
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Elsevier Science Bv
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For the model of independence in a two way contingency table, shrinkage estimators based on minimum phi-divergence estimators and phi-divergence statistics are considered. These estimators are based on the James-Stein-type rule and incorporate the idea of preliminary test estimator. The asymptotic bias and risk are obtained under contiguous alternative hypotheses, and on the basis of them a comparison study is carried out.
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Ali, S.M., Silvey, S.D., 1966.A general class of coefficients of divergence of one distribution from another. J. Roy. Statist. Soc. Ser. B 28, 131–142. Bancroft, T.A., 1944. On biases in estimation due to use of preliminary tests of significance. Ann. Math. Statist. 15, 190–204. Bancroft, T.A., Han, C.P., 1977. Inference based on conditional specification: a note and a bibliography. Internat. Statist. Rev. 45, 117–127. Csiszàr, I., 1963. Eine Informationstheorestiche Ungleichung und ihre Anwendung auf den Beweis der Ergodizität von Markoffschen Ketten. Publ. Math. Inst. Hungarian Acad. Sci. Ser. A 8, 84–108. Ewens,W.J., Grant, G.R., 2005. Statistical Methods in Bioinformatics. Springer, Berlin. Gupta, A., Saleh, A.K.Md., Sen, P.K., 1989. Improved estimation in a contingency table: independence structure. J. Amer. Statist. Assoc. 84, 525–532. Han, C.P., Rao, C.V., Ravichandran, J., 1988. Inference based on conditional specification: a second bibliography. Comm. Statis. Theory Methods 17, 1945–1964. James, W., Stein, C., 1961. Estimation with quadratic loss. In: Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability.University of California Press, Berkeley, CA, pp. 361–379. Judge, G.G., Bock, M.E., 1978. The Statistical Implications of Pretest and Stein-Rule Estimators in Econometrics. North-Holland, Amsterdam. Lehmann, E.L., 1998. Elements of Large-Sample Theory. Springer, Berlin. Menéndez, M., Morales, D., Pardo, L., Vajda, I., 2001. Approximations to powers of phi-disparity goodness-of-fit tests. Comm. Statist. Theory Methods 30 (1), 105–134. Menéndez, M.L., Pardo, J.A., Pardo, L., Zografos, K., 2006. On tests of independence based on minimum phi-divergence estimator with constraints: an application to modeling DNA. Comput. Statist. Data Anal. 51, 1100–1118. Morales, D., Pardo, L., Vajda, I., 1995. Asymptotic divergence of estimates of discrete distributions. J. Statist. Plann. Inference 48 (3), 347–369. Pardo, L., 2006. Statistical Inference Based on Divergence Measures. Chapman & Hall, CRC, NewYork. Saleh, A.K.Md.E., 2006. Theory of Preliminary Test and Stein-Type Estimation with Applications.Wiley, NewYork. Stein, C., 1956. Inadmissibility of the usual estimator for the mean of a multivariate normal distribution. In: Proceeding of the Third Berkeley Symposium on Mathematical Statistics and Probability, vol. 1. University of California Press, Berkeley, CA, pp. 197–206. Vajda, I., 1989. Theory of Statistical Inference and Information. Kluwer Academic Publishers, Dordrecht.
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