Heavy tails - extreme events more common than expected - are everywhere, but they are still treated as mysterious and confusing because the necessary mathematical models are not widely known. For the first time, this book provides a rigorous introduction to heavy-tailed distributions accessible to anyone who knows elementary probability.
Heavy tails - extreme events more common than expected - are everywhere, but they are still treated as mysterious and confusing because the necessary mathematical models are not widely known. For the first time, this book provides a rigorous introduction to heavy-tailed distributions accessible to anyone who knows elementary probability.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Produktdetails
Produktdetails
Cambridge Series in Statistical and Probabilistic Mathematics
Jayakrishnan Nair is Associate Professor in Electrical Engineering at IIT Bombay. His research focuses on modeling, performance evaluation, and design issues in online learning environments, communication networks, queueing systems, and smart power grids. He is the recipient of best paper awards at IFIP Performance (2010 and 2020) and ACM e-Energy (2020).
Inhaltsangabe
Commonly used notation 1. Introduction Part I. Properties: 2. Scale invariance, power laws, and regular variation 3. Catastrophes, conspiracies, and subexponential distributions 4. Residual lives, hazard rates, and long tails Part II. Emergence: 5. Additive processes 6. Multiplicative processes 7. Extremal processes Part III. Estimation: 8. Estimating power-law distributions: Listen to the body 9. Estimating power-law tails: Let the tail do the talking References Index.
Commonly used notation 1. Introduction Part I. Properties: 2. Scale invariance, power laws, and regular variation 3. Catastrophes, conspiracies, and subexponential distributions 4. Residual lives, hazard rates, and long tails Part II. Emergence: 5. Additive processes 6. Multiplicative processes 7. Extremal processes Part III. Estimation: 8. Estimating power-law distributions: Listen to the body 9. Estimating power-law tails: Let the tail do the talking References Index.
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