Heavy-Tailed Distributions in Disaster Analysis

Heavy-Tailed Distributions in Disaster Analysis
Author :
Publisher : Springer Science & Business Media
Total Pages : 199
Release :
ISBN-10 : 9789048191710
ISBN-13 : 9048191718
Rating : 4/5 (10 Downloads)

Book Synopsis Heavy-Tailed Distributions in Disaster Analysis by : V. Pisarenko

Download or read book Heavy-Tailed Distributions in Disaster Analysis written by V. Pisarenko and published by Springer Science & Business Media. This book was released on 2010-07-20 with total page 199 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mathematically, natural disasters of all types are characterized by heavy tailed distributions. The analysis of such distributions with common methods, such as averages and dispersions, can therefore lead to erroneous conclusions. The statistical methods described in this book avoid such pitfalls. Seismic disasters are studied, primarily thanks to the availability of an ample statistical database. New approaches are presented to seismic risk estimation and forecasting the damage caused by earthquakes, ranging from typical, moderate events to very rare, extreme disasters. Analysis of these latter events is based on the limit theorems of probability and the duality of the generalized Pareto distribution and generalized extreme value distribution. It is shown that the parameter most widely used to estimate seismic risk – Mmax, the maximum possible earthquake value – is potentially non-robust. Robust analogues of this parameter are suggested and calculated for some seismic catalogues. Trends in the costs inferred by damage from natural disasters as related to changing social and economic situations are examined for different regions. The results obtained argue for sustainable development, whereas entirely different, incorrect conclusions can be drawn if the specific properties of the heavy-tailed distribution and change in completeness of data on natural hazards are neglected. This pioneering work is directed at risk assessment specialists in general, seismologists, administrators and all those interested in natural disasters and their impact on society.


Heavy-Tailed Distributions in Disaster Analysis Related Books

Heavy-Tailed Distributions in Disaster Analysis
Language: en
Pages: 199
Authors: V. Pisarenko
Categories: Science
Type: BOOK - Published: 2010-07-20 - Publisher: Springer Science & Business Media

DOWNLOAD EBOOK

Mathematically, natural disasters of all types are characterized by heavy tailed distributions. The analysis of such distributions with common methods, such as
Statistical Analysis of Natural Disasters and Related Losses
Language: en
Pages: 89
Authors: V.F. Pisarenko
Categories: Nature
Type: BOOK - Published: 2013-09-11 - Publisher: Springer Science & Business Media

DOWNLOAD EBOOK

The study of disaster statistics and disaster occurrence is a complicated interdisciplinary field involving the interplay of new theoretical findings from sever
The Fundamentals of Heavy Tails
Language: en
Pages: 266
Authors: Jayakrishnan Nair
Categories: Mathematics
Type: BOOK - Published: 2022-06-09 - Publisher: Cambridge University Press

DOWNLOAD EBOOK

Heavy tails –extreme events or values more common than expected –emerge everywhere: the economy, natural events, and social and information networks are jus
Multi-Fractal Traffic and Anomaly Detection in Computer Communications
Language: en
Pages: 297
Authors: Ming Li
Categories: Mathematics
Type: BOOK - Published: 2022-12-29 - Publisher: CRC Press

DOWNLOAD EBOOK

This book provides a comprehensive theory of mono- and multi-fractal traffic, including the basics of long-range dependent time series and 1/f noise, ergodicity
Handbook Of Heavy-tailed Distributions In Asset Management And Risk Management
Language: en
Pages: 598
Authors: Michele Leonardo Bianchi
Categories: Business & Economics
Type: BOOK - Published: 2019-03-08 - Publisher: World Scientific

DOWNLOAD EBOOK

The study of heavy-tailed distributions allows researchers to represent phenomena that occasionally exhibit very large deviations from the mean. The dynamics un