bsdp: Calculate Bootstrap Estimates and Confidence Intervals for the Discrete Pareto Distribution

Description

Uses the parametric bootstrap to estimate the bias and confidence interval of the MLE of the Discrete Pareto Distribution.

Usage

bsdp(x, cutoff=1, m=200, np=1, alpha=0.95)
bootstrapdp(x,cutoff=1,cutabove=1000,
                          m=200,alpha=0.95,guess=3.31,hellinger=FALSE,
                          mle.meth="adpmle")

Arguments

A vector of counts (one per observation).

cutoff

Calculate estimates conditional on exceeding this value.

Number of bootstrap samples to draw.

Number of parameters in the model (1 by default).

alpha

Type I error for the confidence interval.

hellinger

Minimize Hellinger distance of the parametric model from the data instead of maximizing the likelihood.

cutabove

Calculate estimates conditional on not exceeding this value.

guess

Initial estimate at the MLE.

mle.meth

Method to use to compute the MLE.

Value

distmatrix of sample CDFs, one per row.
obsmleThe Discrete Pareto MLE of the PDF exponent.
bsmlesVector of bootstrap MLE.
quantilesQuantiles of the bootstrap MLEs.
pvaluep-value of the Anderson-Darling statistics relative to the bootstrap MLEs.
obsmandsObserved Anderson-Darling Statistic.
meanmlesMean of the bootstrap MLEs.
guessInitial estimate at the MLE.
mle.methMethod to use to compute the MLE.

References

Jones, J. H. and Handcock, M. S. "An assessment of preferential attachment as a mechanism for human sexual network formation," Proceedings of the Royal Society, B, 2003, 270, 1123-1128.

Examples

Run this code

# Now, simulate a Discrete Pareto distribution over 100
# observations with expected count 1 and probability of another
# of 0.2

set.seed(1)
s4 <- simdp(n=100, v=3.31)
table(s4)

#
# Calculate the MLE and an asymptotic confidence
# interval for the parameter.
#

s4est <- adpmle(s4)
s4est

#
# Use the bootstrap to compute a confidence interval rather than using the 
# asymptotic confidence interval for the parameter.
#

bsdp(s4, m=20)