Title :
Nonparametric estimation with quantized data
Author :
Pawlak, M. ; Stadtmüller, U.
fDate :
29 Jun-4 Jul 1997
Abstract :
The accuracy lost to data quantization in the context of nonparametric density, regression and classification estimation problems is considered. We make use of quantization strategies which can be described by a certain auxiliary distribution (quantization density) function. The statistical accuracy of the resulting estimators is studied, i.e., we derive the pointwise and global L2-distance results for the closeness of the estimators to both the true functions and the estimators based on the original unquantized data set
Keywords :
estimation theory; nonparametric statistics; pattern classification; quantisation (signal); accuracy; auxiliary distribution function; classification estimation; data quantization; global L2-distance results; nonparametric density estimation; nonparametric estimation; pointwise results; quantization density function; quantized data; regression; statistical accuracy; true functions; Communication system control; Data acquisition; Data processing; Density functional theory; Error analysis; Information theory; Kernel; Random variables; Smoothing methods; Vector quantization;
Conference_Titel :
Information Theory. 1997. Proceedings., 1997 IEEE International Symposium on
Conference_Location :
Ulm
Print_ISBN :
0-7803-3956-8
DOI :
10.1109/ISIT.1997.613453
