fun.theo.mv.gld

Computes the "mean","variance","skewness","kurtosis" statistics from a 
 given generalised lambda distribution.

univar

The fitting algorithms considered in this package have two major objectives. One is to provide a smoothing device to fit distributions to data using the weight and unweighted discretised approach based on the bin width of the histogram. The other is to provide a definitive fit to the data set using the maximum likelihood and quantile matching estimation. Other methods such as moment matching, starship method, L moment matching are also provided. Diagnostics on goodness of fit can be done via qqplots, KS-resample tests and comparing mean, variance, skewness and kurtosis of the data with the fitted distribution. References include the following: Karvanen and Nuutinen (2008) "Characterizing the generalized lambda distribution by L-moments" <doi:10.1016/j.csda.2007.06.021>, King and MacGillivray (1999) "A starship method for fitting the generalised lambda distributions" <doi:10.1111/1467-842X.00089>, Su (2005) "A Discretized Approach to Flexibly Fit Generalized Lambda Distributions to Data" <doi:10.22237/jmasm/1130803560>, Su (2007) "Nmerical Maximum Log Likelihood Estimation for Generalized Lambda Distributions" <doi:10.1016/j.csda.2006.06.008>, Su (2007) "Fitting Single and Mixture of Generalized Lambda Distributions to Data via Discretized and Maximum Likelihood Methods: GLDEX in R" <doi:10.18637/jss.v021.i09>, Su (2009) "Confidence Intervals for Quantiles Using Generalized Lambda Distributions" <doi:10.1016/j.csda.2009.02.014>, Su (2010) "Chapter 14: Fitting GLDs and Mixture of GLDs to Data using Quantile Matching Method" <doi:10.1201/b10159>, Su (2010) "Chapter 15: Fitting GLD to data using GLDEX 1.0.4 in R" <doi:10.1201/b10159>, Su (2015) "Flexible Parametric Quantile Regression Model" <doi:10.1007/s11222-014-9457-1>, Su (2021) "Flexible parametric accelerated failure time model"<doi:10.1080/10543406.2021.1934854>.

Steve Su

GLDEX

Fitting Single and Mixture of Generalised Lambda Distributions

Martin Maechler

Juha Karvanen

Robert King

Benjamin Dean

R Core Team

fun.theo.mv.gld function

<dl> <dt>L1</dt>
<dd>Lambda 1. Or c(Lambda 1,Lambda 2,Lambda 3,Lambda 4).</dd> <dt>L2</dt>
<dd>Lambda 2.</dd> <dt>L3</dt>
<dd>Lambda 3.</dd> <dt>L4</dt>
<dd>Lambda 4.</dd> <dt>param</dt>
<dd>"rs" or "fmkl" or "fkml"</dd> <dt>normalise</dt>
<dd>"Y" if you want kurtosis to be calculated with reference
 to kurtosis = 0 under Normal distribution.</dd></dl>

Arguments

Author

Find the theoretical first four moments of the generalised lambda 
distribution. — fun.theo.mv.gld

<dl>

 <dt>L1</dt>
<dd>Lambda 1. Or c(Lambda 1,Lambda 2,Lambda 3,Lambda 4).</dd>

 <dt>L2</dt>
<dd>Lambda 2.</dd>

 <dt>L3</dt>
<dd>Lambda 3.</dd>

 <dt>L4</dt>
<dd>Lambda 4.</dd>

 <dt>param</dt>
<dd>"rs" or "fmkl" or "fkml"</dd>

 <dt>normalise</dt>
<dd>"Y" if you want kurtosis to be calculated with reference
 to kurtosis = 0 under Normal distribution.</dd>

</dl>

fun.theo.mv.gld: Find the theoretical first four moments of the generalised lambda distribution.

Description

Usage

Value

Arguments

Author

References

See Also

Examples