dparetoll: Maximum Likelihood Estimation for the Discrete Pareto...

In tolerance: Statistical Tolerance Intervals and Regions

Description

Performs maximum likelihood estimation for the parameter of the discrete Pareto distribution.

Usage

dpareto.ll(x, theta = NULL, ...) 

Arguments

x	A vector of raw data which is distributed according to a Poisson-Lindley distribution.
theta	Optional starting value for the parameter. If NULL, then the method of moments estimator is used.
...	Additional arguments passed to the mle function.

Details

The discrete Pareto distribution is a discretized of the continuous Type II Pareto distribution (also called the Lomax distribution).

Value

See the help file for mle to see how the output is structured.

References

Krishna, H. and Pundir, P. S. (2009), Discrete Burr and Discrete Pareto Distributions, Statistical Methodology, 6, 177–188.

Young, D. S., Naghizadeh Qomi, M., and Kiapour, A. (2019), Approximate Discrete Pareto Tolerance Limits for Characterizing Extremes in Count Data, Statistica Neerlandica, 73, 4–21.

See Also

mle, DiscretePareto

Examples

## Maximum likelihood estimation for randomly generated data
## from the discrete Pareto distribution. 

set.seed(100)

dp.data <- rdpareto(n = 500, theta = 0.2)
out.dp <- dpareto.ll(dp.data)
stats4::coef(out.dp)
stats4::vcov(out.dp)

Example output

Warning messages:
1: In rgl.init(initValue, onlyNULL) : RGL: unable to open X11 display
2: 'rgl_init' failed, running with rgl.useNULL = TRUE 
3: .onUnload failed in unloadNamespace() for 'rgl', details:
  call: fun(...)
  error: object 'rgl_quit' not found 
    theta 
0.1889001 
            [,1]
[1,] 0.000215344

tolerance documentation built on Feb. 6, 2020, 5:08 p.m.