TY - JOUR
T1 - Kernel estimators for cell probabilities
AU - Grund, Birgit
PY - 1993/8
Y1 - 1993/8
N2 - Kernel density estimators for discrete multivariate data are investigated, using the notation framework of contingency tables. We derive large sample properties of kernel estimators and the least-squares cross-validation method for choosing the bandwidth, including the asymptotic bias, the mean summed squared error, the actual summed squared error, and the asymptotic distribution of the resulting non-parametric estimator. We show that the least-squares cross-validation procedure is superior to Kullback-Leibler cross-validation in terms of mean summed squared error, but that the least-squares cross-validation is still sub-optimal concerning actual summed squared error.
AB - Kernel density estimators for discrete multivariate data are investigated, using the notation framework of contingency tables. We derive large sample properties of kernel estimators and the least-squares cross-validation method for choosing the bandwidth, including the asymptotic bias, the mean summed squared error, the actual summed squared error, and the asymptotic distribution of the resulting non-parametric estimator. We show that the least-squares cross-validation procedure is superior to Kullback-Leibler cross-validation in terms of mean summed squared error, but that the least-squares cross-validation is still sub-optimal concerning actual summed squared error.
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U2 - 10.1006/jmva.1993.1062
DO - 10.1006/jmva.1993.1062
M3 - Article
AN - SCOPUS:38248999901
SN - 0047-259X
VL - 46
SP - 283
EP - 308
JO - Journal of Multivariate Analysis
JF - Journal of Multivariate Analysis
IS - 2
ER -