Geodesic
L1-optimal Statistical Discrimination Procedures and their Asymptotic Properties
Mathematica Applicanda, Tome 17 (1989) no. 31, pp. 57-89.

Voir la notice de l'article provenant de la source Annales Societatis Mathematicae Polonae Series

We consider the generalized Z^-norm optimization problem assuming that the joint probability distri-bution of random variables is unknown. The solution to the problem has, therefore, to be estimated from a sample. We examine a natural estimator and show its strong consistency and asymptotic normality under quite general assumptions. Certain discrimination and screening problems, formalized in decision- theoretical manner, can be solved using Z^-norm minimization procedures. We derive asymptotic expansions of risk corresponding to estimated solu-tions.
DOI : 10.14708/ma.v17i31.1765
Classification : 62H30 (62E20 68T10)
Mots-clés : Classification and discrimination, cluster analysis, Asymptotic distribution theory, Pattern recognition, speech recognition 
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Wojciech Niemiro. L1-optimal Statistical Discrimination Procedures and their Asymptotic Properties. Mathematica Applicanda, Tome 17 (1989) no. 31, pp.  57-89. doi : 10.14708/ma.v17i31.1765. http://geodesic.mathdoc.fr/articles/10.14708/ma.v17i31.1765/

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