WKURTOSIS Computes sample kurtosis CALL: k = wkurtosis(X,dim); k = sample kurtosis (fourth central moment divided by squared second) X = data vector or matrix dim = dimension to sum across. (default 1'st non-singleton dimension of X) Example: R=wgumbrnd(2,2,100,2); wkurtosis(R) See also wskewness, mean, var
% CHAPTER2 Modelling random loads and stochastic waves | |
Estimate transformation, g, from data. |
001 function k = wkurtosis(X,dim) 002 %WKURTOSIS Computes sample kurtosis 003 % 004 % CALL: k = wkurtosis(X,dim); 005 % 006 % k = sample kurtosis (fourth central moment divided by squared second) 007 % X = data vector or matrix 008 % dim = dimension to sum across. (default 1'st non-singleton 009 % dimension of X) 010 % 011 % Example: 012 % R=wgumbrnd(2,2,100,2); 013 % wkurtosis(R) 014 % 015 % See also wskewness, mean, var 016 017 % Tested on: Matlab 5.3 018 % History: 019 % revised pab 24.10.2000 020 % - made it more general: accepts any size of X 021 % - added dim, nargchk 022 % added ms 16.06.2000 023 024 error(nargchk(1,2,nargin)) 025 sz = size(X); 026 if nargin<2|isempty(dim) 027 % Use 1'st non-singleton dimension or dimension 1 028 dim = min(find(sz~=1)); 029 if isempty(dim), dim = 1; end 030 end 031 rsz = ones(size(sz)); rsz(dim)=sz(dim); 032 mu = mean(X); 033 mu = repmat(mu,rsz); 034 k = mean((X-mu).^4,dim)./mean((X-mu).^2,dim).^2; 035 036 037 038 039 040 041 042
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