Inference For Heavy Tailed Data
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Inference for Heavy Tailed Data
Author | : Liang Peng,Yongcheng Qi |
Publsiher | : Academic Press |
Total Pages | : 180 |
Release | : 2017-08-11 |
Genre | : Mathematics |
ISBN | : 9780128047507 |
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Heavy tailed data appears frequently in social science, internet traffic, insurance and finance. Statistical inference has been studied for many years, which includes recent bias-reduction estimation for tail index and high quantiles with applications in risk management, empirical likelihood based interval estimation for tail index and high quantiles, hypothesis tests for heavy tails, the choice of sample fraction in tail index and high quantile inference. These results for independent data, dependent data, linear time series and nonlinear time series are scattered in different statistics journals. Inference for Heavy-Tailed Data Analysis puts these methods into a single place with a clear picture on learning and using these techniques. Contains comprehensive coverage of new techniques of heavy tailed data analysis Provides examples of heavy tailed data and its uses Brings together, in a single place, a clear picture on learning and using these techniques
The Fundamentals of Heavy Tails
Author | : Jayakrishnan Nair,Adam Wierman,Bert Zwart |
Publsiher | : Cambridge University Press |
Total Pages | : 265 |
Release | : 2022-06-09 |
Genre | : Business & Economics |
ISBN | : 9781316511732 |
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An accessible yet rigorous package of probabilistic and statistical tools for anyone who must understand or model extreme events.
A Practical Guide to Heavy Tails
Author | : Robert Adler,Raya Feldman,Murad Taqqu |
Publsiher | : Springer Science & Business Media |
Total Pages | : 560 |
Release | : 1998-10-26 |
Genre | : Mathematics |
ISBN | : 0817639519 |
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Twenty-four contributions, intended for a wide audience from various disciplines, cover a variety of applications of heavy-tailed modeling involving telecommunications, the Web, insurance, and finance. Along with discussion of specific applications are several papers devoted to time series analysis, regression, classical signal/noise detection problems, and the general structure of stable processes, viewed from a modeling standpoint. Emphasis is placed on developments in handling the numerical problems associated with stable distribution (a main technical difficulty until recently). No index. Annotation copyrighted by Book News, Inc., Portland, OR
Nonparametric Analysis of Univariate Heavy Tailed Data
Author | : Natalia Markovich |
Publsiher | : John Wiley & Sons |
Total Pages | : 336 |
Release | : 2008-03-11 |
Genre | : Mathematics |
ISBN | : 0470723599 |
Download Nonparametric Analysis of Univariate Heavy Tailed Data Book in PDF, Epub and Kindle
Heavy-tailed distributions are typical for phenomena in complex multi-component systems such as biometry, economics, ecological systems, sociology, web access statistics, internet traffic, biblio-metrics, finance and business. The analysis of such distributions requires special methods of estimation due to their specific features. These are not only the slow decay to zero of the tail, but also the violation of Cramer’s condition, possible non-existence of some moments, and sparse observations in the tail of the distribution. The book focuses on the methods of statistical analysis of heavy-tailed independent identically distributed random variables by empirical samples of moderate sizes. It provides a detailed survey of classical results and recent developments in the theory of nonparametric estimation of the probability density function, the tail index, the hazard rate and the renewal function. Both asymptotical results, for example convergence rates of the estimates, and results for the samples of moderate sizes supported by Monte-Carlo investigation, are considered. The text is illustrated by the application of the considered methodologies to real data of web traffic measurements.
Heavy Tailed Distributions and Robustness in Economics and Finance
Author | : Marat Ibragimov,Rustam Ibragimov,Johan Walden |
Publsiher | : Springer |
Total Pages | : 131 |
Release | : 2015-05-23 |
Genre | : Business & Economics |
ISBN | : 9783319168777 |
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This book focuses on general frameworks for modeling heavy-tailed distributions in economics, finance, econometrics, statistics, risk management and insurance. A central theme is that of (non-)robustness, i.e., the fact that the presence of heavy tails can either reinforce or reverse the implications of a number of models in these fields, depending on the degree of heavy-tailed ness. These results motivate the development and applications of robust inference approaches under heavy tails, heterogeneity and dependence in observations. Several recently developed robust inference approaches are discussed and illustrated, together with applications.
Topics in Identification Limited Dependent Variables Partial Observability Experimentation and Flexible Modeling
Author | : Ivan Jeliazkov,Justin Tobias |
Publsiher | : Emerald Group Publishing |
Total Pages | : 252 |
Release | : 2019-10-18 |
Genre | : Business & Economics |
ISBN | : 9781838674212 |
Download Topics in Identification Limited Dependent Variables Partial Observability Experimentation and Flexible Modeling Book in PDF, Epub and Kindle
Volume 40B of Advances in Econometrics examines innovations in stochastic frontier analysis, nonparametric and semiparametric modeling and estimation, A/B experiments, big-data analysis, and quantile regression.
Bayesian Inference
Author | : Javier Prieto Tejedor |
Publsiher | : BoD – Books on Demand |
Total Pages | : 379 |
Release | : 2017-11-02 |
Genre | : Mathematics |
ISBN | : 9789535135777 |
Download Bayesian Inference Book in PDF, Epub and Kindle
The range of Bayesian inference algorithms and their different applications has been greatly expanded since the first implementation of a Kalman filter by Stanley F. Schmidt for the Apollo program. Extended Kalman filters or particle filters are just some examples of these algorithms that have been extensively applied to logistics, medical services, search and rescue operations, or automotive safety, among others. This book takes a look at both theoretical foundations of Bayesian inference and practical implementations in different fields. It is intended as an introductory guide for the application of Bayesian inference in the fields of life sciences, engineering, and economics, as well as a source document of fundamentals for intermediate Bayesian readers.
Heavy Tail Phenomena
Author | : Sidney I. Resnick |
Publsiher | : Springer Science & Business Media |
Total Pages | : 412 |
Release | : 2007-12-03 |
Genre | : Mathematics |
ISBN | : 9780387450247 |
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This comprehensive text gives an interesting and useful blend of the mathematical, probabilistic and statistical tools used in heavy-tail analysis. It is uniquely devoted to heavy-tails and emphasizes both probability modeling and statistical methods for fitting models. Prerequisites for the reader include a prior course in stochastic processes and probability, some statistical background, some familiarity with time series analysis, and ability to use a statistics package. This work will serve second-year graduate students and researchers in the areas of applied mathematics, statistics, operations research, electrical engineering, and economics.