Cramér-Chernoff Theorem for L1-norm in Kernel Density Estimator for Two Independent Samples Teorema de Cramér-Chernoff para la norma L1 del estimador núcleo para dos muestras independientes
In this paper a Chernoff type theorem for the L1 distance between kernel estimators from two independent and identically distributed random samples is developed. The harmonic mean is used to correct the distance for inequal sample sizes case. Moreover, the proved result is used to compute the Bahadu...
Main Authors: | TERESA LÓPEZ, NORBERTO CORRAL, PABLO MARTÍNEZ-CAMBLOR |
---|---|
Format: | Article |
Language: | English |
Published: |
Universidad Nacional de Colombia
2009-01-01
|
Series: | Revista Colombiana de Estadística |
Subjects: | |
Online Access: | http://www.scielo.org.co/scielo.php?script=sci_arttext&pid=S0120-17512009000200007 |
Similar Items
-
Bahadur Efficiencies for Statistics of Truncated P-value Combination Methods
by: Chen, Xiaohui
Published: (2018) -
Asymptotics for L 1 $L_{1}$ -wavelet method for nonparametric regression
by: Xingcai Zhou, et al.
Published: (2020-09-01) -
Bahadur representations of M-estimators and their applications in general linear models
by: Hongchang Hu
Published: (2018-05-01) -
Minimum Divergence Estimators, Maximum Likelihood and the Generalized Bootstrap
by: Michel Broniatowski
Published: (2021-01-01) -
Surfaces quantile : propriétés, convergences et applications
by: Ahidar-Coutrix, Adil
Published: (2015)