 Dataplot Vol 2 Vol 1

# L MOMENTS

Name:
L MOMENTS (LET)
Type:
Let Subcommand
Purpose:
Compute the sample L-moment ratios of a variable.
Description:
Given a random variable X with a cumulative distribution function F, the probability weighted moments are defined to be: Two special cases are  The L-moments are defined as with denoting the rth shifted Legendre ploynomials. The L-moment estimators are linear combinations of order statistics.

L-moments are related to probability weighted moments by the equation where with denoting the binomal coefficient. The L-moment ratios are defined to be This command returns the L-moment estimates for orders 1 and 2 and L-moment ratios for higher orders.

For an ordered sample x1:n <= x2:n <= ... <= xn:n, unbiased estimators of   The primary use of probability weighted moments and L-moments is in the estimation of parameters for a probability distribution. Estimates based on probability weighted moments and L-moments are generally superior to standard moment-based estimates. The L-moment estimators have some desirable properties for parameter estimation. In particular, they are robust with respect to outliers and their small sample bias tends to be small. L-moment estimators can often be used when the maximum likelihood estimates are unavailable, difficult to compute, or have undesirable properties. They may also be used as starting values for maximum likelihood estimates.

Estimation methods based on L-moments are discussed in the papers listed in the Reference section below (Dataplot generates L-moment based estimates for the maximum likelihood estimates for the generalized Pareto and the generalized extreme value distributions).

Syntax:
LET <y> = L MOMENTS <x> <nmom>
<SUBSET/EXCEPT/FOR qualification>
where <x> is the response variable;
<nmom> is the number of L-moments that will be generated;
<y> is a variable where the computed L-moments are saved;
and where the <SUBSET/EXCEPT/FOR qualification> is optional.
Examples:
LET XMOM = L MOMENTS Y 5
LET XMOM = L MOMENTS Y 4 SUBSET Y > 0
Note:
Dataplot computes L-moments using the SAMLMU routine written by Hoskings and documented in "Research Report: Fortran Routines for use with the Method of L-Moments" (see the Reference section below).

Hoskings software provides L-moment estimators for 11 different distributions. Dataplot currently implements the L-moment estimates for the generalized Pareto and generalized extreme value distributions. To obtain these estimates, enter the commands

GENERALIZED PARETO MAXIMUM LIKELIHOOD Y
GENERALIZED EXTREME VALUE MAXIMUM LIKELIOHOOD Y

We plan to implement the L-moment estimators for additional distributions in future releases of Dataplot.

Default:
If the <nmom> parameter is omitted from the command, the first four sample L-moments are computed.
Synonyms:
None
Related Commands:
 PROBABILITY WEIGHTED MOMENTS = Compute the sample probability weighted moments of a variable. MAXIMUM LIKELIHOOD = Compute maximum likelihood estimates for a probability distribution.
References:
"Research Report: Fortran Routines for use with the Method of L-Moments", J. R. M. Hosking, IBM Research Division, T. J. Watson Research Center, Yorktown Heights, NY 10598, 6/2000.

"L-moments: Analysis and Estimation of Distribution using Linear Combinations of Order Statistics", Hoskings, Journal of the Royal Statistical Society, Series B, 52, 1990, pp. 105-124.

"The Estimation of Extreme Quantiles of Wind Velocity Using L-Moments in the Peaks-Over-Threshold Approach", Pandey, Van Gelder, and Vrijling, Structural Safety, 23, 2001, pp. 179-192.

"Probability Weighted Moments: Definition and Relation to Parameters of Several Distributions Expressable in Inverse Form", Greenwood, Landwehr, Matalas, and Wallis, Water Resources Research, 15, 1979, 1049-1054.

"Estimation of the Generalized Extreme Value Distribution by the Method of Probability-Weighte Moments", Hosking, Wallis, and Wood, Technometrics, 27, 1985, 251-261.

"Probability Weighted Moments Compared with Some Traditional Techniques in Estimating Gumbel Parameters and Quantiles", Landwehr, Matalas, Wallis, Water Resources Research, 15, (1979a), 1055-1064.

"Extreme Value and Related Models with Applications in Engineering and Science", Castillo, Hadi, Balakrishnan, and Sarabia, Wiley, 2005, pp. 117-119.

Applications:
Distributional Modeling
Implementation Date:
2005/6
Program:
```
LET GAMMA = -0.3
LET Y = GENERALIZED PARETO RANDOM NUMBERS FOR I = 1 1 100
LET Y = 5*Y
LET NMOM = 3
LET XMOM = L MOMENTS Y NMOM
PRINT XMOM
GENERALIZED PARETO MLE Y
```
The following output is generated
```

VARIABLES--XMOM

0.4160070E+01
-0.3669064E-01
-0.4920776E+00

GENERALIZED PARETO PARAMETER ESTIMATION:

SUMMARY STATISTICS:
NUMBER OF OBSERVATIONS                     =      100
SAMPLE MEAN (ALL DATA)                     =    4.160070
SAMPLE VARIANCE (ALL DATA)                 =    9.603471
SAMPLE MINIMUM                             =   0.7464492E-02
SAMPLE MAXIMUM                             =    12.82312
USER-SPECIFIED THRESHOLD                   =    0.000000

METHOD OF MOMENTS:
(VALID IF SHAPE PARAMETER < 1)
SAMPLE MEAN (AFTER SUBTRACT LOCATION)      =    4.152607
SAMPLE VARIANCE (AFTER SUBTRACT LOCATION)  =    9.603471
ESTIMATE OF LOCATION                       =   0.7463745E-02
ESTIMATE OF SCALE                          =    5.804547
ESTIMATE OF GAMMA                          =  -0.3978080
VARIANCE OF GAMMA                          =   0.1448942E-01
VARIANCE OF SCALE                          =   0.6819228
COVARIANCE OF GAMMA AND SCALE              =   0.8959930E-01

L-MOMENTS:
(L-MOMENT ESTIMATES WORK BEST FOR
VALUES OF SHAPE PARAMETER IN (-0.5,0.5))
FIRST SAMPLE L-MOMENT                      =    4.160070
SECOND SAMPLE L-MOMENT                     =    1.718239
THIRD SAMPLE L-MOMENT                      =   0.2119006
ESTIMATE OF LOCATION                       =   0.2070879
ESTIMATE OF SCALE                          =    5.141252
ESTIMATE OF SHAPE                          =  -0.3006007

ELEMENTAL PERCENTILE METHOD:
ESTIMATE OF LOCATION                       =   0.7463745E-02
ESTIMATE OF SCALE                          =    5.473905
ESTIMATE OF GAMMA                          =  -0.4396029

MAXIMUM LIKELIHOOD:
(MAXIMUM LIKELIHOOD ESTIMATES DO
NOT EXIST IF SHAPE PARAMTER < -1
AND MAY PERFORM POORLY IF < -0.5)
ESTIMATE OF LOCATION                       =   0.7463745E-02
ESTIMATE OF SCALE                          =    3.383862
ESTIMATE OF GAMMA                          =  -0.2713313
VARIANCE OF GAMMA                          =   0.1616283E-01
VARIANCE OF SCALE                          =   0.2911481
COVARIANCE OF GAMMA AND SCALE              =   0.4302010E-01

NOTE: FOR THE GENERALIZED PARETO DISTRIBUTION, LARGE SAMPLE SIZES
MAY BE REQUIRED FOR THE NORMAL APPROXIMATIONS TO BE ACCURATE (E.G., > 500).

CONFIDENCE INTERVAL FOR SCALE PARAMETER

NORMAL APPROXIMATION
CONFIDENCE           LOWER         UPPER
VALUE (%)            LIMIT         LIMIT
-------------------------------------------
50.000           3.01992       3.74780
75.000           2.76315       4.00457
90.000           2.49633       4.27139
95.000           2.32630       4.44142
99.000           1.99399       4.77373
99.900           1.60836       5.15936

CONFIDENCE INTERVAL FOR SHAPE PARAMETER

NORMAL APPROXIMATION
CONFIDENCE           LOWER         UPPER
VALUE (%)            LIMIT         LIMIT
-------------------------------------------
50.000          -.357081      -.185581
75.000          -.417579      -.125084
90.000          -.480447      -.622159E-01
95.000          -.520508      -.221549E-01
99.000          -.598805      0.561420E-01
99.900          -.689665      0.147002
```

Date created: 1/6/2006
Last updated: 1/6/2006
Please email comments on this WWW page to alan.heckert@nist.gov.