%0 Journal Article
%T Parameter estimation of K distribution based on second-kind statistics<br>基于第二类统计量的K分布参数估计
%A SUN Zeng-guo
%A <br>孙增国
%J 计算机应用研究
%D 2013
%I 
%X In order to efficiently estimate the parameters of K distribution, this paper proposed the log-cumulant estimator. Based on second-kind statistics, first it derived the second-kind first characteristic function of K distribution by taking the Mellin transformation to the probability density function of K distribution, second obtained the second-kind second cha-racteristic function of K distribution from the logarithmic transformation of the second-kind first characteristic function, and last deduced the first two log-cumulants to estimate the parameters of K distribution by taking the derivative of the second-kind second characteristic function. Compared to the traditional maximum likelihood estimator, the log-cumulant estimator of K distribution with analytical expressions was easy to compute. Monte Carlo simulations demonstrate that the log-cumulant estimator of K distribution based on second-kind statistics achieves high estimation accuracy.
%K K distribution
%K Mellin transformation
%K second-kind statistics
%K log-cumulant estimator
%K Monte Carlo simulations<br>K分布
%K Mellin变换
%K 第二类统计量
%K 对数累积量估计
%K Monte
%K Carlo仿真
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=A9D9BE08CDC44144BE8B5685705D3AED&aid=51EFA6F8AD10024358A17468314748FF&yid=FF7AA908D58E97FA&vid=340AC2BF8E7AB4FD&iid=CA4FD0336C81A37A&sid=D997634CFE9B6321&eid=B6DA1AC076E37400&journal_id=1001-3695&journal_name=计算机应用研究&referenced_num=0&reference_num=11