 Dataplot Vol 2 Vol 1

# LP LOCATION

Name:
LP LOCATION (LET)
Type:
Let Subcommand
Purpose:
Compute the Lp (least power) location estimate of a variable.
Description:
This description is summarized from the more thorough discussion given in the Pennecchi and Callegaro paper (see the Reference section below).

The univariate measurement model (or location model) is

$$y_i = \alpha + e_i$$

where $$\alpha$$ is the unknown value to be estimated and the yi are the sample observations affected by the measurement errors ei.

The least power (Lp) provides a broad class of location estimators. This class includes the mean, the median, and the mid-range as special cases.

The Lp norm (for p >= 1) is defined as

$$||x||_p = \left( \sum_{i=1}^{n}{|x_i|^p} \right) ^{(1/p)}$$

For p = 1, 2, and $$\infty$$, these become the following norms

$$||x||_p = \left( \sum_{i=1}^{n}{|x_i|^p} \right) ^{(1/p)}$$
$$||x||_2 = \sqrt{\sum_{i=1}^{n}{x_i^{2}}}$$
$$||x||_1 = \sum_{i=1}^{n}{|x_i|}$$

The Lp norm estimation is based on the minimization of the Lp norm of a suitable residual vector. Specifically, the Lp estimator of $$\alpha$$ is

$$\left\{ L_{p}(x_i) = \arg_{\alpha} \min \left( \sum_{i=1}^{n}{|x_i - \alpha|^p} \right) ^{1/p} \right\}$$

where arg min means the argument of the minimum. That is, the value of $$\alpha$$ that results in the minimum value of the expression.

The Lp estimate is the solution of the equation

$$\sum_{i=1}^{n}{|x_i - \alpha|^{p-1}\mbox{sign}(x_i - \alpha)} = 0$$

The special cases mentioned above correspond to

 p = 1 - sample median p = 2 - sample mean p = $$\infty$$ - sample mid-range

Values of p between 1 and 2 are of most interest as these have efficiency and robustness properties between the median (p = 1) and the mean (p = 2).

The Pennecchi and Callegaro paper provides the following guidelines for choosing a suitable value for p. Compute the sample kurtosis, $$\hat{k}$$, of the sample observations (note that the standard kurtosis formula should be used, not the version that subtracts 3 to make the kurtosis of a normal distribution equal to 0). Then

 $$\hat{k}$$ < 2.2 - use the mid-range (i.e., p = $$\infty$$ ) 2.2 ≤ $$\hat{k}$$ ≤ 3 - use the mean (i.e., p = 2) 3 < $$\hat{k}$$ < 6 - use p = 1.5 $$\hat{k}$$ ≥ 6 - use the median (i.e., p = 1)

Pennecchi and Callegaro propose the following as an estimate of the asymptotic variance

$$\frac{m(2p - 2)}{\left( (p-1)m(p-2)\right) ^{2}}/n$$

where

$$m(r) = \frac{1}{n} \sum_{i=1}^{n}{|x_i - L_p(x_i)|^r}$$
Syntax 1:
LET <par> = LP LOCATION <y>
<SUBSET/EXCEPT/FOR qualification>
where <y> is the response variable;
<par> is a parameter where the computed lp location value is saved;
and where the <SUBSET/EXCEPT/FOR qualification> is optional.

Use this syntax to compute the Lp location estimate.

Syntax 2:
LET <par> = VARIANCE OF LP LOCATION <y>
<SUBSET/EXCEPT/FOR qualification>
where <y> is the response variable;
<par> is a parameter where the computed variance of the lp location value is saved;
and where the <SUBSET/EXCEPT/FOR qualification> is optional.

Use this syntax to compute the variance of the Lp location estimate.

Syntax 3:
LET <par> = SD OF LP LOCATION <y>
<SUBSET/EXCEPT/FOR qualification>
where <y> is the response variable;
<par> is a parameter where the computed sd of the lp location value is saved;
and where the <SUBSET/EXCEPT/FOR qualification> is optional.

Use this syntax to compute the standard deviation of the Lp location estimate.

Examples:
LET P = 1.5
LET ALOC = LP LOCATION Y
LET AVAR = LP VARIANCE Y
LET ASD = LP SD Y
LET ALOC = LP LOCATION Y SUBSET Y > 0
Note:
Specify the value of p (before using the LP LOCATION or LP VARIANCE commands) by entering the following command

LET P = <value>
Note:
Dataplot statistics can be used in a number of commands. For details, enter

Default:
None
Synonyms:
STANDARD DEVIATION OF LP LOCATION is a synonym for SD OF LP LOCATION
SD LP LOCATION is a synonym for SD OF LP LOCATION
VARIANCE LP LOCATION is a synonym for VARIANCE OF LP LOCATION
Related Commands:
 MEAN = Compute the mean of a variable. MEDIAN = Compute the median of a variable. MIDRANGE = Compute the midrange of a variable. H15 LOCATION = Compute the H15 estimate of location. VARIANCE = Compute the variance of a variable. STANDARD DEVIATION = Compute the standard deviation of a variable. MAD = Compute the median absolute deviation of a variable.
Applications:
Data Analysis, Key Comparisons
Reference:
Francesca Pennecchi and Luca Callegaro (2006), "Between the Mean and the Median: the Lp Estimator," Metrologia, 43, pp. 213-219.
Implementation Date:
2007/11
Program:

LET Y1 = NORMAL RANDOM NUMBERS FOR I = 1 1 50
LET Y2 = LAPLACE RANDOM NUMBERS FOR I = 1 1 50
LET Y3 = UNIFORM RANDOM NUMBERS FOR I = 1 1 50
LET Y4 = SLASH RANDOM NUMBERS FOR I = 1 1 50
LET Y X = STACKED Y1 Y2 Y3 Y4
.
MULTIPLOT SCALE FACTOR 2
MULTIPLOT CORNER COORDINATES 5 5 95 95
LABEL CASE ASIS
TIC MARK LABEL CASE ASIS
TITLE CASE ASIS
TITLE OFFSET 2
Y1LABEL DISPLACEMENT 15
XLIMITS 1 4
MAJOR XTIC MARK NUMBER 4
MINOR XTIC MARK NUMBER 0
X1TIC MARK LABEL FORMAT ALPHA
X1TIC MARK LABEL CONTENT Normal Laplace Uniform Slash
TIC MARK OFFSET UNITS DATA
X1TIC MARK OFFSET 0.5 0.5
CHARACTER X BLANK
LINE BLANK SOLID
.
MULTIPLOT 2 2
LET P = 1
Y1LABEL L(1) Location
LP LOCATION PLOT Y X
LET P = 1.5
Y1LABEL L(1.5) Location
LP LOCATION PLOT Y X
LET P = 2
Y1LABEL L(2) Location
LP LOCATION PLOT Y X
LET P = 100
Y1LABEL L(100) Location
LP LOCATION PLOT Y X
END OF MULTIPLOT
.
SET WRITE DECIMALS 4
SET LET CROSS TABULATE COLLAPSE
LET P = 1.5
LET XGROUP = CROSS TABULATE GROUP ONE X
LET YMEAN = CROSS TABULATE LP LOCATION Y X
LET YSD   = CROSS TABULATE SD OF LP LOCATION Y X
PRINT XGROUP YMEAN YSD

The following output is generated ---------------------------------------------
XGROUP          YMEAN            YSD
---------------------------------------------
1.0000        -0.0176         0.1124
2.0000        -0.0066         0.2120
3.0000         0.5542         0.0641
4.0000        -3.1948         3.6755


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Date created: 07/14/2011
Last updated: 10/07/2016