When Do I Take Which Distribution?: A Statistical Basis for Entrepreneurial Applications

This document was uploaded by one of our users. The uploader already confirmed that they had the permission to publish it. If you are author/publisher or own the copyright of this documents, please report to us by using this DMCA report form.

Simply click on the Download Book button.

Yes, Book downloads on Ebookily are 100% Free.

Sometimes the book is free on Amazon As well, so go ahead and hit "Search on Amazon"

This book provides the statistical basis for quantitative risk management by presenting and explaining the most important distributions. Distributions describe the occurrence and impact of a risk. They are a prerequisite for risk aggregation, risk analysis and risk assessment as required by the German revision standards IDW PS 340, StaRUG and FISG. This book portrays the distributions that are fundamental for enterprise risk management and shows when and how they are used. These include the Bernoulli distribution, the binomial distribution, the Poisson distribution, the uniform distribution, the triangular distribution, the PERT distribution, the modified PERT distribution, the trapezoidal distribution, the custom distribution, the normal distribution, the lognormal distribution, the Weibull distribution, the expert distribution, the poly distribution and the compound distribution. Furthermore, the book explains how the parameterisation of the distributions can be done via expert estimates or algorithmic calibration.

Author(s): Uwe Wehrspohn, Dietmar Ernst
Series: SpringerBriefs in Business
Publisher: Springer
Year: 2022

Language: English
Pages: 50
City: Cham

Preface
Contents
Chapter 1: Distributions for the Occurrence of the Risk
1.1 Bernoulli Distribution
1.2 Binomial Distribution
1.3 Poisson Distribution
Chapter 2: Distributions for the Effects of Risk
2.1 Constant Distribution
2.2 The Uniform Distribution
2.3 Triangular, PERT, and Modified PERT Distribution
2.4 Trapezoidal Distribution
2.5 Custom Distributions
2.6 Normal Distribution
2.7 Lognormal Distribution
2.8 Weibull Distribution
2.9 Expert Distributions
2.10 Poly Distributions
2.11 Automatic Calibration of the Distributions
2.12 Compound Distribution
Chapter 3: Application of Distributions in ERM