L1-optimal Statistical Discrimination Procedures and their Asymptotic Properties

Wojciech Niemiro. "L1-optimal Statistical Discrimination Procedures and their Asymptotic Properties." Mathematica Applicanda 17.31 (1989): null. <http://eudml.org/doc/292679>.

@article{WojciechNiemiro1989,
abstract = {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.},
author = {Wojciech Niemiro},
journal = {Mathematica Applicanda},
keywords = {Classification and discrimination, cluster analysis; Asymptotic distribution theory; Pattern recognition, speech recognition},
language = {eng},
number = {31},
pages = {null},
title = {L1-optimal Statistical Discrimination Procedures and their Asymptotic Properties},
url = {http://eudml.org/doc/292679},
volume = {17},
year = {1989},
}

TY - JOUR
AU - Wojciech Niemiro
TI - L1-optimal Statistical Discrimination Procedures and their Asymptotic Properties
JO - Mathematica Applicanda
PY - 1989
VL - 17
IS - 31
SP - null
AB - 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.
LA - eng
KW - Classification and discrimination, cluster analysis; Asymptotic distribution theory; Pattern recognition, speech recognition
UR - http://eudml.org/doc/292679
ER -