R help - Mar 2009 - Fw: Estimating Parameters of Weibull and Pareto distribution using LMOM package

Dear R helpers
 
 I have r file which estimate the parameters of 3 parameter
 Weibull  - 
 
 (A)  - continuous shape parameter (alpha)
      - continuous scale parameter (beta)
      - continuous location parameter (gamma)
 
 (B) Also, I have a r file which calculates the parameters
 of Generalized Pareto distribution. 
 
      - location parameter xi, 
      - scale parameter alpha and 
      - shape parameter k
 
 
 However, If I have to use the same files for estimating
 parameters of 2 parameter Weibull and 2 parameter Pareto distribution,   how do
I use it?
 
 
 I am giving the R script I am using to calculate the Generalized Pareto
distribution as
 
 library(lmom)
 
 amounts <- (10023.47, 10171.42,13446.83,10263.49,10219.07, 10025.71,
10318.88, 10034.85,10004.98,10012.72)
 
 lmom 		        	<- samlmu(amounts); lmom
 
 parameters_of_Gen_Pareto   <- pelgpa(lmom);
 parameters_of_Gen_Pareto
 
 
 The parameters estimated are
 
   xi        alpha            k 
 9993.3131812   81.9540457   -0.8213843 
 
 
 If the location paramter xi = 0, then this becomes two
 parameter Paretom distribution. However, it is my gut
 feeling that if xi = 0, other parameter values will also
 change.
 
 Please help me.
 
 Regards and thanking in advance
 
 Maithili

Maithili Shiva wrote:
>  Dear R helpers
>  
>  I have r file which estimate the parameters of 3 parameter
>  Weibull  - 
>  
>  (A)  - continuous shape parameter (alpha)
>       - continuous scale parameter (beta)
>       - continuous location parameter (gamma)
>  
>  (B) Also, I have a r file which calculates the parameters
>  of Generalized Pareto distribution. 
>  
>       - location parameter xi, 
>       - scale parameter alpha and 
>       - shape parameter k
>  
>  
>  However, If I have to use the same files for estimating
>  parameters of 2 parameter Weibull and 2 parameter Pareto distribution,  
how do I use it?
>  
>  
>  I am giving the R script I am using to calculate the Generalized Pareto
distribution as
>  
>  library(lmom)
>  
>  amounts <- (10023.47, 10171.42,13446.83,10263.49,10219.07, 10025.71,
10318.88, 10034.85,10004.98,10012.72)
>  
>  lmom 		        	<- samlmu(amounts); lmom
>  
>  parameters_of_Gen_Pareto   <- pelgpa(lmom);
>  parameters_of_Gen_Pareto
>  
>  
>  The parameters estimated are
>  
>    xi        alpha            k 
>  9993.3131812   81.9540457   -0.8213843 
>  
>  
>  If the location paramter xi = 0, then this becomes two
>  parameter Paretom distribution. However, it is my gut
>  feeling that if xi = 0, other parameter values will also
>  change.
pelgpa allows you to specify the lower bound of the distribution and 
estimate the other two parameters.  Compare

   > pelgpa(samlmu(amounts))
             xi        alpha            k
   9993.3131812   81.9540457   -0.8213843

and

   > pelgpa(samlmu(amounts),bound=10000)
             xi        alpha            k
   10000.000000    72.993343    -0.838561

pelwei offers similar options for the Weibull distribution.


J. R. M. Hosking


>  Please help me.
>  
>  Regards and thanking in advance
>  
>  Maithili
> 
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