Calculates a smoothed version of the median.
-- Function File: M = smoothmedian (X)
-- Function File: M = smoothmedian (X, DIM)
-- Function File: M = smoothmedian (X, DIM, TOL)
If X is a vector, find the univariate smoothed median (M) of X. If X is a
matrix, compute the univariate smoothed median value for each column and
return them in a row vector. If the optional argument DIM is given,
operate along this dimension. Arrays of more than two dimensions are not
NaN values currently supported. The MEX file versions of this function
ignore (omit) whereas the m-file includes NaN in it's calculations. Use
the 'which' command to establish which version of the function is being
used.
The smoothed median is a slightly smoothed version of the ordinary median
and is an M-estimator that is both robust and efficient:
| Asymptotic | Mean | Median | Median |
| properties | | (smoothed) | (ordinary) |
|---------------------------------------|------|------------|------------|
| Breakdown point | 0.00 | 0.341 | 0.500 |
| Pitman efficacy | 1.00 | 0.865 | 0.637 |
Smoothing the median is achieved by minimizing the objective function:
S (M) = sum (((X(i) - M).^2 + (X(j) - M).^2).^ 0.5)
i < j
where i and j refers to the indices of the Cartesian product of each
column of X with itself.
With the ordinary median as the initial value of M, this function
minimizes the above objective function by finding the root of the first
derivative using a fast, but reliable, Newton-Bisection hybrid algorithm.
The tolerance (TOL) is the maximum value of the step size that is
acceptable to break from optimization. By default, TOL = range * 1e-04.
The smoothing works by slightly reducing the breakdown point of the median.
Bootstrap confidence intervals using the smoothed median have good
coverage for the ordinary median of the population distribution and can be
used to obtain second order accurate intervals with Studentized bootstrap
and calibrated percentile bootstrap methods [1]. When the population
distribution is thought to be strongly skewed, coverage errors can be
reduced by improving symmetry through appropriate data transformation.
Unlike kernel-based smoothing approaches, bootstrapping smoothmedian does
not require explicit choice of a smoothing parameter or a probability
density function.
Bibliography:
[1] Brown, Hall and Young (2001) The smoothed median and the
bootstrap. Biometrika 88(2):519-534
smoothmedian (version 2023.05.02)
Author: Andrew Charles Penn
https://www.researchgate.net/profile/Andrew_Penn/
Copyright 2019 Andrew Charles Penn
This program 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 program 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 this program. If not, see http://www.gnu.org/licenses/
Package: statistics-resampling