Title :
Rough histograms for robust statistics
Author :
Strauss, Olivier ; Comby, Fr?©d?©ric ; Aldon, Marie-Jos?©
Author_Institution :
LIRMM, Univ. des Sci. et Tech. du Languedoc, Montpellier, France
fDate :
6/22/1905 12:00:00 AM
Abstract :
Applied statistics are widely used in pattern recognition and other computing applications to find the most likely value of a parameter. The use of classical empirical statistics is based upon assumption about normality of underlying density distribution of data. When the data is corrupted by contaminated noise, then classical tools are usually not robust enough and the estimation of the mode is biased. In this article, we propose to estimate the main mode of a distribution by means of a rough histogram and we show that this estimation is robust to contamination
Keywords :
estimation theory; fuzzy set theory; noise; pattern recognition; probability; statistical analysis; density distribution; estimation theory; fuzzy set theory; noise; pattern recognition; rough histogram; statistical analysis; Computer applications; Density functional theory; Histograms; Noise robustness; Pattern recognition; Postal services; Probability; Random variables; Statistical distributions; Statistics;
Conference_Titel :
Pattern Recognition, 2000. Proceedings. 15th International Conference on
Print_ISBN :
0-7695-0750-6
DOI :
10.1109/ICPR.2000.906167