aout.pareto {alphaOutlier}

Find \alpha-outliers in Pareto data

Description

Given the parameters of a Pareto distribution, aout.pareto identifies \alpha-outliers in a given data set.

Usage

aout.pareto(data, param, alpha = 0.1, hide.outliers = FALSE)

Arguments

data

a vector. The data set to be examined.

param

a vector. Contains the parameters of the Pareto distribution: \lambda, \theta.

alpha

an atomic vector. Determines the maximum amount of probability mass the outlier region may contain. Defaults to 0.1.

hide.outliers

boolean. Returns the outlier-free data if set to TRUE. Defaults to FALSE.

Details

We use the Pareto distribution with Lebesgue-density f(x) = \frac{\lambda \theta^{\lambda}}{x^{\lambda + 1}}.

Value

Data frame of the input data and an index named is.outlier that flags the outliers with TRUE. If hide.outliers is set to TRUE, a simple vector of the outlier-free data.

Author(s)

A. Rehage

References

Gather, U.; Kuhnt, S.; Pawlitschko, J. (2003) Concepts of outlyingness for various data structures. In J. C. Misra (Ed.): Industrial Mathematics and Statistics. New Delhi: Narosa Publishing House, 545-585.

See Also

citiesData

Examples

data(citiesData)
aout.pareto(citiesData[[1]], c(1.31, 14815), alpha = 0.01)