Abstract
In this work, parametric measures of information (information matrices) are defined from the nonparametric information measures called ((h, π)) -divergences. Asymptotic distributions for information matrices are obtained when the parameter is replaced by its maximum likelihood estimator. On the basis of these results, tests of hypotheses are constructed.
The research in this paper was supported in part by DGICYT Grants N. PB93-0068 and N. PB93-0022. Their financial support is gratefully acknowledged
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Morales, D., Pardo, L., Salicrú, M., Menéndez, M.L. (1995). Information matrices associated to (h, π)-divergence measures: Applications to testing hypotheses. In: Bouchon-Meunier, B., Yager, R.R., Zadeh, L.A. (eds) Advances in Intelligent Computing — IPMU '94. IPMU 1994. Lecture Notes in Computer Science, vol 945. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0035955
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DOI: https://doi.org/10.1007/BFb0035955
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