ITE / code / H_I_D_A_C / meta_estimators / ASpearman_L_estimation.m

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function [A] = ASpearman_L_estimation(Y,ds,co)
%Estimates lower tail dependence based on the conditional Spearman's rho.
%   Y: Y(:,t) is the t^th sample.
%  ds: subspace dimensions.
%  co: association measure estimator object.
%   1)We use the naming convention 'A<name>_estimation' to ease embedding new association measure estimator methods.
%   2)This is a meta method: the conditional Spearman's rho estimator (of lower tail) can be arbitrary.
%   Friedrich Schmid and Rafael Schmidt. Multivariate conditional versions of Spearman's rho and related measures of tail dependence. Journal of Multivariate Analysis, 98:1123-1140, 2007.
%   C. Spearman. The proof and measurement of association between two things. The American Journal of Psychology, 15:72-101, 1904.
%Copyright (C) 2012 Zoltan Szabo ("", "szzoli (at) cs (dot) elte (dot) hu")
%This file is part of the ITE (Information Theoretical Estimators) Matlab/Octave toolbox.
%ITE is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by
%the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
%This software is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of
%MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General Public License for more details.
%You should have received a copy of the GNU General Public License along with ITE. If not, see <>.


    if sum(ds) ~= size(Y,1);
        error('The subspace dimensions are not compatible with Y.');
    if ~one_dimensional_problem(ds)
        error('The subspaces must be one-dimensional for this estimator.');

    num_of_samples = size(Y,2);
    k = floor(sqrt(num_of_samples));
    co.member_co.p = k/num_of_samples; %set p

A = A_estimation(Y,ds,co.member_co);