order_iparalogistic: Random Sampling of k-th Order Statistics from a Inverse...

View source: R/order_iparalogistic.R

order_iparalogisticR Documentation

Random Sampling of k-th Order Statistics from a Inverse Paralogistic Distribution

Description

order_iparalogistic is used to obtain a random sample of the k-th order statistic from a Inverse Paralogistic distribution and some associated quantities of interest.

Usage

order_iparalogistic(size, k, shape, scale, n, p = 0.5, alpha = 0.05, ...)

Arguments

size

numeric, represents the size of the sample.

k

numeric, represents the k-th smallest value from a sample.

shape

numeric, represents a first shape parameter value. Must be strictly positive.

scale

numeric, represents scale parameter values. Must be strictly positive.

n

numeric, represents the size of the sample to compute the order statistic from.

p

numeric, represents the 100p percentile for the distribution of the k-th order statistic. Default value is population median, p = 0.5.

alpha

numeric, (1 - alpha) represents the confidence of an interval for the population percentile p of the distribution of the k-th order statistic. Default value is 0.05.

...

represents others parameters of a Inverse Paralogistic distribution.

Value

A list with a random sample of order statistics from a Inverse Paralogistic Distribution, the value of its join probability density function evaluated in the random sample and an approximate (1 - alpha) confidence interval for the population percentile p of the distribution of the k-th order statistic.

Author(s)

Carlos Alberto Cardozo Delgado <cardozorpackages@gmail.com>.

References

Gentle, J, Computational Statistics, First Edition. Springer - Verlag, 2009.

Klugman, S. A., Panjer, H. H. and Willmot, G. E. (2012), Loss Models, From Data to Decisions, Fourth Edition, Wiley.

Examples

library(orders)
# A sample of size 10 of the 3-th order statistics from a Inverse Paralogistic Distribution
order_iparalogistic(size=10,shape=1.5,scale=0.5,k=3,n=50,p=0.5,alpha=0.02)

orders documentation built on Nov. 14, 2023, 9:07 a.m.