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# kendall

## PURPOSE

% Compute Kendall's @var{tau} for each of the variables specified by

## SYNOPSIS

function tau = kendall (x, y)

## DESCRIPTION

% -*- texinfo -*-
% @deftypefn {Function File} {} kendall (@var{x}, @var{y})
% Compute Kendall's @var{tau} for each of the variables specified by
% the input arguments.
%
% For matrices, each row is an observation and each column a variable;
% vectors are always observations and may be row or column vectors.
%
% @code{kendall (@var{x})} is equivalent to @code{kendall (@var{x},
% @var{x})}.
%
% For two data vectors @var{x}, @var{y} of common length @var{n},
% Kendall's @var{tau} is the correlation of the signs of all rank
% differences of @var{x} and @var{y};  i.e., if both @var{x} and
% @var{y} have distinct entries, then
%
% @iftex
% @tex
% $$\tau = {1 \over n(n-1)} \sum_{i,j} {\rm sign}(q_i-q_j) {\rm sign}(r_i-r_j)$$
% @end tex
% @end iftex
% @ifnottex
% @example
% @group
%          1
% tau = -------   SUM sign (q(i) - q(j)) * sign (r(i) - r(j))
%       n (n-1)   i,j
% @end group
% @end example
% @end ifnottex
%
% @noindent
% in which the
% @iftex
% @tex
% $q_i$ and $r_i$
% @end tex
% @end iftex
% @ifnottex
% @var{q}(@var{i}) and @var{r}(@var{i})
% @end ifnottex
%  are the ranks of
% @var{x} and @var{y}, respectively.
%
% If @var{x} and @var{y} are drawn from independent distributions,
% Kendall's @var{tau} is asymptotically normal with mean 0 and variance
% @iftex
% @tex
% ${2 (2n+5) \over 9n(n-1)}$.
% @end tex
% @end iftex
% @ifnottex
% @code{(2 * (2@var{n}+5)) / (9 * @var{n} * (@var{n}-1))}.
% @end ifnottex
% @end deftypefn

## CROSS-REFERENCE INFORMATION

This function calls:
• cor % Compute correlation.
• ranks % Return the ranks of @var{x} along the first non-singleton dimension
This function is called by:

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