The Laplace Distribution And Generalizations PDF Download
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Author | : Samuel Kotz |
Publisher | : Springer Science & Business Media |
Total Pages | : 358 |
Release | : 2012-12-06 |
Genre | : Mathematics |
ISBN | : 146120173X |
Download The Laplace Distribution and Generalizations Book in PDF, ePub and Kindle
This book describes the inferential and modeling advantages that this distribution, together with its generalizations and modifications, offers. The exposition systematically unfolds with many examples, tables, illustrations, and exercises. A comprehensive index and extensive bibliography also make this book an ideal text for a senior undergraduate and graduate seminar on statistical distributions, or for a short half-term academic course in statistics, applied probability, and finance.
Author | : Samuel Kotz |
Publisher | : |
Total Pages | : |
Release | : 2001 |
Genre | : |
ISBN | : 9783764341664 |
Download The Laplace Distribution and Generalizations Book in PDF, ePub and Kindle
Author | : Samuel Kotz |
Publisher | : Birkhäuser |
Total Pages | : 349 |
Release | : 2001-05-18 |
Genre | : Mathematics |
ISBN | : 9780817641665 |
Download The Laplace Distribution and Generalizations Book in PDF, ePub and Kindle
This book describes the inferential and modeling advantages that this distribution, together with its generalizations and modifications, offers. The exposition systematically unfolds with many examples, tables, illustrations, and exercises. A comprehensive index and extensive bibliography also make this book an ideal text for a senior undergraduate and graduate seminar on statistical distributions, or for a short half-term academic course in statistics, applied probability, and finance.
Author | : Tomasz J. Kozubowski |
Publisher | : |
Total Pages | : 0 |
Release | : 2001 |
Genre | : |
ISBN | : |
Download A Multivariate and Asymmetric Generalization of Laplace Distribution Book in PDF, ePub and Kindle
Consider a sum of independent and identically distributed random vectors with finite second moments, where the number of terms has a geometric distribution independent of the summands. We show that the class of limiting distributions of such random sums, as the number of terms converges to infinity, consists of multivariate asymmetric distributions that are natural generalizations of univariate Laplace laws. We call these limits multivariate asymmetric Laplace laws. We give an explicit form of their multidimensional densities and show representations that effectively facilitate computer simulation of variates from this class. We also discuss the relation to other formerly considered classes of distributions containing Laplace laws.
Author | : N. Balakrishnan |
Publisher | : Springer Science & Business Media |
Total Pages | : 552 |
Release | : 2006-05-17 |
Genre | : Mathematics |
ISBN | : |
Download Advances in Distribution Theory, Order Statistics, and Inference Book in PDF, ePub and Kindle
The purpose of this book is to honor the fundamental contributions to many different areas of statistics made by Barry Arnold. Distinguished and active researchers highlight some of the recent developments in statistical distribution theory, order statistics and their properties, as well as inferential methods associated with them. Applications to survival analysis, reliability, quality control, and environmental problems are emphasized.
Author | : Lee S Dewald (Sr) |
Publisher | : |
Total Pages | : 33 |
Release | : 1988 |
Genre | : |
ISBN | : |
Download L-Laplace Processes Book in PDF, ePub and Kindle
A broad family of symmetric, thick tailed distributions, the small l-Laplace distributions, is described. These are natural generalizations of the Laplace distribution. A family of random coefficient Autoregressive Moving Average processes with small l-Laplace marginal distributions is constructed and its properties are explored. Extensions to ARIMA processes are considered. The first order autoregression with a standard Laplace marginal distribution is examined in detail and different estimates of the autoregressive parameter are compared theoretically and by simulation. Keywords: Random variables. (kr).
Author | : Cynthia Dwork |
Publisher | : |
Total Pages | : 286 |
Release | : 2014 |
Genre | : Computers |
ISBN | : 9781601988188 |
Download The Algorithmic Foundations of Differential Privacy Book in PDF, ePub and Kindle
The problem of privacy-preserving data analysis has a long history spanning multiple disciplines. As electronic data about individuals becomes increasingly detailed, and as technology enables ever more powerful collection and curation of these data, the need increases for a robust, meaningful, and mathematically rigorous definition of privacy, together with a computationally rich class of algorithms that satisfy this definition. Differential Privacy is such a definition. The Algorithmic Foundations of Differential Privacy starts out by motivating and discussing the meaning of differential privacy, and proceeds to explore the fundamental techniques for achieving differential privacy, and the application of these techniques in creative combinations, using the query-release problem as an ongoing example. A key point is that, by rethinking the computational goal, one can often obtain far better results than would be achieved by methodically replacing each step of a non-private computation with a differentially private implementation. Despite some powerful computational results, there are still fundamental limitations. Virtually all the algorithms discussed herein maintain differential privacy against adversaries of arbitrary computational power -- certain algorithms are computationally intensive, others are efficient. Computational complexity for the adversary and the algorithm are both discussed. The monograph then turns from fundamentals to applications other than query-release, discussing differentially private methods for mechanism design and machine learning. The vast majority of the literature on differentially private algorithms considers a single, static, database that is subject to many analyses. Differential privacy in other models, including distributed databases and computations on data streams, is discussed. The Algorithmic Foundations of Differential Privacy is meant as a thorough introduction to the problems and techniques of differential privacy, and is an invaluable reference for anyone with an interest in the topic.
Author | : Jayakrishnan Nair |
Publisher | : Cambridge University Press |
Total Pages | : 266 |
Release | : 2022-06-09 |
Genre | : Mathematics |
ISBN | : 1009062964 |
Download The Fundamentals of Heavy Tails Book in PDF, ePub and Kindle
Heavy tails –extreme events or values more common than expected –emerge everywhere: the economy, natural events, and social and information networks are just a few examples. Yet after decades of progress, they are still treated as mysterious, surprising, and even controversial, primarily because the necessary mathematical models and statistical methods are not widely known. This book, for the first time, provides a rigorous introduction to heavy-tailed distributions accessible to anyone who knows elementary probability. It tackles and tames the zoo of terminology for models and properties, demystifying topics such as the generalized central limit theorem and regular variation. It tracks the natural emergence of heavy-tailed distributions from a wide variety of general processes, building intuition. And it reveals the controversy surrounding heavy tails to be the result of flawed statistics, then equips readers to identify and estimate with confidence. Over 100 exercises complete this engaging package.
Author | : Hans Fischer |
Publisher | : Springer Science & Business Media |
Total Pages | : 415 |
Release | : 2010-10-08 |
Genre | : Mathematics |
ISBN | : 0387878572 |
Download A History of the Central Limit Theorem Book in PDF, ePub and Kindle
This study discusses the history of the central limit theorem and related probabilistic limit theorems from about 1810 through 1950. In this context the book also describes the historical development of analytical probability theory and its tools, such as characteristic functions or moments. The central limit theorem was originally deduced by Laplace as a statement about approximations for the distributions of sums of independent random variables within the framework of classical probability, which focused upon specific problems and applications. Making this theorem an autonomous mathematical object was very important for the development of modern probability theory.
Author | : Roman Vershynin |
Publisher | : Cambridge University Press |
Total Pages | : 299 |
Release | : 2018-09-27 |
Genre | : Business & Economics |
ISBN | : 1108415199 |
Download High-Dimensional Probability Book in PDF, ePub and Kindle
An integrated package of powerful probabilistic tools and key applications in modern mathematical data science.