# data2normpoly: Estimation of normal-polynomial quantile mixture In Lmoments: L-Moments and Quantile Mixtures

## Description

Estimates the parameters of normal-polynomial quantile mixture from data or from L-moments

## Usage

 ```1 2 3 4``` ```data2normpoly4(data) lmom2normpoly4(lmom) data2normpoly6(data) lmom2normpoly6(lmom) ```

## Arguments

 `data` matrix or data frame `lmom` vector or matrix of L-moments

## Value

vector or matrix containing the four or six parameters of normal-polynomial quantile mixture

## Author(s)

Juha Karvanen [email protected]

## References

Karvanen, J. 2006. Estimation of quantile mixtures via L-moments and trimmed L-moments, Computational Statistics & Data Analysis 51, (2), 947–959. http://www.bsp.brain.riken.jp/publications/2006/karvanen_quantile_mixtures.pdf.

`dnormpoly` for L-moments, `dnormpoly` for the normal-polynomial quantile mixture and `data2cauchypoly4` for the estimation of Cauchy-polynomial quantile mixture.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13``` ```#Generates a sample 500 observations from the normal-polynomial quantile mixture, #calculates L-moments and their covariance matrix, #estimates parameters via L-moments and #plots the true pdf and the estimated pdf together with the histogram of the data. true_params<-lmom2normpoly4(c(0,1,0.2,0.05)); x<-rnormpoly(500,true_params); lmoments<-Lmoments(x); lmomcov<-Lmomcov(x); estim_params<-lmom2normpoly4(lmoments); hist(x,30,freq=FALSE); plotpoints<-seq(min(x)-1,max(x)+1,by=0.01); lines(plotpoints,dnormpoly(plotpoints,estim_params),col='red'); lines(plotpoints,dnormpoly(plotpoints,true_params),col='blue'); ```

### Example output

```
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Lmoments documentation built on May 2, 2019, 2:04 a.m.