Title :
On the Estimating Burr XII Distribution Parameters
Author :
Abbasi, B. ; Hosseinifard, S.Z. ; Abdollahian, M.
Author_Institution :
Dept. of Stat. & Oper. Res., RMIT Univ., Melbourne, VIC, Australia
Abstract :
Burr XII distribution plays an important role in reliability modeling, risk analyzing and process capability estimation. However, estimating two parameters of the Burr XII distribution, i.e., c and k, is a complicated task and using conventional methods is not straightforward. In this paper a neural network to estimate Burr XII parameters is presented. The inputs of proposed neural network are skewness and kurtosis. The performance of proposed methods is evaluated in different simulation examples.
Keywords :
multilayer perceptrons; parameter estimation; statistical distributions; Burr XII distribution parameter estimation; neural network; process capability estimation; reliability modeling; risk analyzing; Artificial neural networks; Density functional theory; Failure analysis; Information analysis; Multi-layer neural network; Multilayer perceptrons; Neural networks; Neurons; Parameter estimation; Shape; Burr XII distribution; Parameter estimation; artificial neural network;
Conference_Titel :
Information Technology: New Generations (ITNG), 2010 Seventh International Conference on
Conference_Location :
Las Vegas, NV
Print_ISBN :
978-1-4244-6270-4
DOI :
10.1109/ITNG.2010.165
