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In mathematics, the '''Lehmer mean''' of a [[tuple]] <math>x</math> of positive [[real number]]s, named after [[Derrick Henry Lehmer]],<ref>P. S. Bullen. ''Handbook of means and their inequalities''. Springer, 1987.</ref> is defined as:
:<math>L_p(x) = \frac{\sum_{k=1}^n x_k^p}{\sum_{k=1}^n x_k^{p-1}}.</math>
 
The '''weighted Lehmer mean''' with respect to a tuple <math>w</math> of positive weights is defined as:
:<math>L_{p,w}(x) = \frac{\sum_{k=1}^n w_k\cdot x_k^p}{\sum_{k=1}^n w_k\cdot x_k^{p-1}}.</math>
 
The Lehmer mean is an alternative to [[power mean]]s
for [[Interpolation|interpolating]] between [[minimum]] and [[maximum]] via [[arithmetic mean]] and [[harmonic mean]].
 
== Properties ==
 
The derivative of <math>p \mapsto L_p(x)</math> is non-negative
:<math>
\frac{\partial}{\partial p} L_p(x) =
\frac
  {\sum_{j=1}^{n}\sum_{k=j+1}^{n}
      (x_j-x_k)\cdot(\ln x_j - \ln x_k)\cdot(x_j\cdot x_k)^{p-1}}
  {\left(\sum_{k=1}^{n} x_k^{p-1}\right)^2},
</math>
thus this function is monotonic and the inequality
:<math>p\le q \Rightarrow L_p(x) \le L_q(x)</math>
holds.
 
==Special cases==
 
*<math>\lim_{p\to-\infty} L_p(x)</math> is the [[minimum]] of the elements of <math>x</math>.
*<math>L_0(x)</math> is the [[harmonic mean]].
*<math>L_\frac{1}{2}\left((x_0,x_1)\right)</math> is the [[geometric mean]] of the two values <math>x_0</math> and <math>x_1</math>.
*<math>L_1(x)</math> is the [[arithmetic mean]].
*<math>L_2(x)</math> is the [[contraharmonic mean]].
*<math>\lim_{p\to\infty} L_p(x)</math> is the [[maximum]] of the elements of <math>x</math>.
:Sketch of a proof: [[Without loss of generality]] let <math>x_1,\dots,x_k</math> be the values which equal the maximum. Then <math>L_p(x)=x_1\cdot\frac{k+\left(\frac{x_{k+1}}{x_1}\right)^p+\cdots+\left(\frac{x_{n}}{x_1}\right)^p}{k+\left(\frac{x_{k+1}}{x_1}\right)^{p-1}+\cdots+\left(\frac{x_{n}}{x_1}\right)^{p-1}}</math>
 
== Applications ==
 
===Signal processing===
Like a [[power mean]],
a Lehmer mean serves a non-linear [[moving average]] which is shifted towards small signal values for small <math>p</math> and emphasizes big signal values for big <math>p</math>. Given an efficient implementation of a [[lowpass|moving arithmetic mean]] called <tt>smooth</tt> you can implement a moving Lehmer mean
according to the following [[Haskell (programming language)|Haskell]] code.
 
<source lang="haskell">
lehmerSmooth :: Floating a => ([a] -> [a]) -> a -> [a] -> [a]
lehmerSmooth smooth p xs = zipWith (/)
                                    (smooth (map (**p) xs))
                                    (smooth (map (**(p-1)) xs))
</source>
 
* For big <math>p</math> it can serve an [[envelope detector]] on a [[rectifier|rectified]] signal.
* For small <math>p</math> it can serve an [[Baseline (spectrometry)|baseline detector]] on a [[mass spectrum]].
 
==See also==
*[[mean]]
*[[power mean]]
 
==Notes==
{{reflist}}
 
==External links==
*[http://mathworld.wolfram.com/LehmerMean.html Lehmer Mean at MathWorld]
 
[[Category:Means]]
[[Category:Articles with example Haskell code]]

Latest revision as of 01:29, 21 November 2013

In mathematics, the Lehmer mean of a tuple x of positive real numbers, named after Derrick Henry Lehmer,[1] is defined as:

Lp(x)=k=1nxkpk=1nxkp1.

The weighted Lehmer mean with respect to a tuple w of positive weights is defined as:

Lp,w(x)=k=1nwkxkpk=1nwkxkp1.

The Lehmer mean is an alternative to power means for interpolating between minimum and maximum via arithmetic mean and harmonic mean.

Properties

The derivative of pLp(x) is non-negative

pLp(x)=j=1nk=j+1n(xjxk)(lnxjlnxk)(xjxk)p1(k=1nxkp1)2,

thus this function is monotonic and the inequality

pqLp(x)Lq(x)

holds.

Special cases

Sketch of a proof: Without loss of generality let x1,,xk be the values which equal the maximum. Then Lp(x)=x1k+(xk+1x1)p++(xnx1)pk+(xk+1x1)p1++(xnx1)p1

Applications

Signal processing

Like a power mean, a Lehmer mean serves a non-linear moving average which is shifted towards small signal values for small p and emphasizes big signal values for big p. Given an efficient implementation of a moving arithmetic mean called smooth you can implement a moving Lehmer mean according to the following Haskell code.

 lehmerSmooth :: Floating a => ([a] -> [a]) -> a -> [a] -> [a]
 lehmerSmooth smooth p xs = zipWith (/)
                                     (smooth (map (**p) xs))
                                     (smooth (map (**(p-1)) xs))

See also

Notes

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External links

  1. P. S. Bullen. Handbook of means and their inequalities. Springer, 1987.