qSlicedLNormPareto: The inverse cumulative distribution function of a Sliced...

In NetSimR: Actuarial Functions for Non-Life Insurance Modelling

The inverse cumulative distribution function of a Sliced LogNormal Pareto severity distribution

Description

Gives the claim amount that a claim from a sliced severity distribution, with LogNormal attritional claims below the slice point and a Pareto tail above it, stays at or below with probability q; the inverse of pSlicedLNormPareto.

Usage

qSlicedLNormPareto(q, mu, sigma, SlicePoint, shape)

Arguments

q	A real number between 0 and 1 - the probability where the inverse cumulative distribution function will be evaluated. Values outside [0, 1] give NaN with a warning, as in qlnorm().
mu	A real number - the first parameter of the attritional Claim Severity's LogNormal distribution.
sigma	A positive real number - the second parameter of the attritional Claim Severity's LogNormal distribution.
SlicePoint	A positive real number - the slice point and the scale parameter of the tail Claim Severity's Pareto distribution. An infinite slice point gives the LogNormal distribution.
shape	A positive real number - the shape parameter of the tail Claim Severity's Pareto distribution.

Details

shape is the Pareto shape parameter, usually written alpha; the sliced Gamma-Pareto functions call the same parameter PShape.

Value

The value of the inverse cumulative distribution function at q with an attritional claim LogNormal distribution with parameters mu and sigma and a large claim Pareto distribution with parameters SlicePoint and shape. A non-numeric argument or a non-positive sigma, SlicePoint or shape is an error; NA values give NA.

See Also

Other sliced distribution functions: SlicedGammaParetoMean(), SlicedLNormParetoMean(), dSlicedGammaPareto(), dSlicedLNormPareto(), pSlicedGammaPareto(), pSlicedLNormPareto(), qSlicedGammaPareto()

Examples

qSlicedLNormPareto(0.5,6,1.5,1000,1.2)
qSlicedLNormPareto(0.7,7,1.6,3000,1.4)

NetSimR documentation built on Sept. 30, 2026, 5:13 p.m.