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Author : Friedrich Liese,Igor Vajda
Publisher : Unknown
Page : 228 pages
File Size : 44,5 Mb
Release : 1987
Category : Convex functions
ISBN : UCAL:B4405790
Author : Pierre Moulin,Venugopal V. Veeravalli
Publisher : Cambridge University Press
Page : 423 pages
File Size : 47,5 Mb
Release : 2019
Category : Mathematics
ISBN : 9781107185920
A mathematically accessible textbook introducing all the tools needed to address modern inference problems in engineering and data science.
Author : Ayanendranath Basu,Hiroyuki Shioya,Chanseok Park
Publisher : CRC Press
Page : 424 pages
File Size : 49,5 Mb
Release : 2011-06-22
Category : Computers
ISBN : 9781420099669
In many ways, estimation by an appropriate minimum distance method is one of the most natural ideas in statistics. However, there are many different ways of constructing an appropriate distance between the data and the model: the scope of study referred to by "Minimum Distance Estimation" is literally huge. Filling a statistical resource gap, Stati
Author : Mark Louis Taper,Jose Miguel Ponciano,Yukihiko Toquenaga,Hidetoshi Shimodaira
Publisher : Frontiers Media SA
Page : 238 pages
File Size : 47,8 Mb
Release : 2022-02-15
Category : Science
ISBN : 9782889744060
Author : Anonim
Publisher : Academic Press
Page : 490 pages
File Size : 45,8 Mb
Release : 2022-07-15
Category : Mathematics
ISBN : 9780323913461
Geometry and Statistics, Volume 46 in the Handbook of Statistics series, highlights new advances in the field, with this new volume presenting interesting chapters written by an international board of authors. Provides the authority and expertise of leading contributors from an international board of authors Presents the latest release in the Handbook of Statistics series Updated release includes the latest information on Geometry and Statistics
Author : Leandro Pardo
Publisher : MDPI
Page : 344 pages
File Size : 43,9 Mb
Release : 2019-05-20
Category : Social Science
ISBN : 9783038979364
This book presents new and original research in Statistical Information Theory, based on minimum divergence estimators and test statistics, from a theoretical and applied point of view, for different statistical problems with special emphasis on efficiency and robustness. Divergence statistics, based on maximum likelihood estimators, as well as Wald’s statistics, likelihood ratio statistics and Rao’s score statistics, share several optimum asymptotic properties, but are highly non-robust in cases of model misspecification under the presence of outlying observations. It is well-known that a small deviation from the underlying assumptions on the model can have drastic effect on the performance of these classical tests. Specifically, this book presents a robust version of the classical Wald statistical test, for testing simple and composite null hypotheses for general parametric models, based on minimum divergence estimators.
Author : Claudio Agostinelli,Ayanendranath Basu,Peter Filzmoser,Diganta Mukherjee
Publisher : Springer
Page : 201 pages
File Size : 42,5 Mb
Release : 2016-11-10
Category : Business & Economics
ISBN : 9788132236436
This book offers a collection of recent contributions and emerging ideas in the areas of robust statistics presented at the International Conference on Robust Statistics 2015 (ICORS 2015) held in Kolkata during 12–16 January, 2015. The book explores the applicability of robust methods in other non-traditional areas which includes the use of new techniques such as skew and mixture of skew distributions, scaled Bregman divergences, and multilevel functional data methods; application areas being circular data models and prediction of mortality and life expectancy. The contributions are of both theoretical as well as applied in nature. Robust statistics is a relatively young branch of statistical sciences that is rapidly emerging as the bedrock of statistical analysis in the 21st century due to its flexible nature and wide scope. Robust statistics supports the application of parametric and other inference techniques over a broader domain than the strictly interpreted model scenarios employed in classical statistical methods. The aim of the ICORS conference, which is being organized annually since 2001, is to bring together researchers interested in robust statistics, data analysis and related areas. The conference is meant for theoretical and applied statisticians, data analysts from other fields, leading experts, junior researchers and graduate students. The ICORS meetings offer a forum for discussing recent advances and emerging ideas in statistics with a focus on robustness, and encourage informal contacts and discussions among all the participants. They also play an important role in maintaining a cohesive group of international researchers interested in robust statistics and related topics, whose interactions transcend the meetings and endure year round.
Author : Ansgar Steland,Ewaryst Rafajłowicz,Ostap Okhrin
Publisher : Springer Nature
Page : 450 pages
File Size : 40,6 Mb
Release : 2019-10-15
Category : Mathematics
ISBN : 9783030286651
This volume presents selected and peer-reviewed contributions from the 14th Workshop on Stochastic Models, Statistics and Their Applications, held in Dresden, Germany, on March 6-8, 2019. Addressing the needs of theoretical and applied researchers alike, the contributions provide an overview of the latest advances and trends in the areas of mathematical statistics and applied probability, and their applications to high-dimensional statistics, econometrics and time series analysis, statistics for stochastic processes, statistical machine learning, big data and data science, random matrix theory, quality control, change-point analysis and detection, finance, copulas, survival analysis and reliability, sequential experiments, empirical processes, and microsimulations. As the book demonstrates, stochastic models and related statistical procedures and algorithms are essential to more comprehensively understanding and solving present-day problems arising in e.g. the natural sciences, machine learning, data science, engineering, image analysis, genetics, econometrics and finance.
Author : J.A. Vísek
Publisher : Springer Science & Business Media
Page : 440 pages
File Size : 48,8 Mb
Release : 2012-12-06
Category : Computers
ISBN : 9789401099134
Author : Jaromír Antoch,Jana Jurečková,Matúš Maciak,Michal Pešta
Publisher : Springer
Page : 207 pages
File Size : 54,5 Mb
Release : 2017-01-24
Category : Mathematics
ISBN : 9783319513133
This volume collects authoritative contributions on analytical methods and mathematical statistics. The methods presented include resampling techniques; the minimization of divergence; estimation theory and regression, eventually under shape or other constraints or long memory; and iterative approximations when the optimal solution is difficult to achieve. It also investigates probability distributions with respect to their stability, heavy-tailness, Fisher information and other aspects, both asymptotically and non-asymptotically. The book not only presents the latest mathematical and statistical methods and their extensions, but also offers solutions to real-world problems including option pricing. The selected, peer-reviewed contributions were originally presented at the workshop on Analytical Methods in Statistics, AMISTAT 2015, held in Prague, Czech Republic, November 10-13, 2015.
Author : Zaven A. Karian,Edward J. Dudewicz
Publisher : CRC Press
Page : 1722 pages
File Size : 40,7 Mb
Release : 2016-04-19
Category : Mathematics
ISBN : 9781584887126
With the development of new fitting methods, their increased use in applications, and improved computer languages, the fitting of statistical distributions to data has come a long way since the introduction of the generalized lambda distribution (GLD) in 1969. Handbook of Fitting Statistical Distributions with R presents the latest and best methods
Author : Frank Nielsen,Frédéric Barbaresco
Publisher : Springer Nature
Page : 641 pages
File Size : 44,9 Mb
Release : 2023-07-31
Category : Computers
ISBN : 9783031382710
This book constitutes the proceedings of the 6th International Conference on Geometric Science of Information, GSI 2023, held in St. Malo, France, during August 30-September 1, 2023. The 125 full papers presented in this volume were carefully reviewed and selected from 161 submissions. They cover all the main topics and highlights in the domain of geometric science of information, including information geometry manifolds of structured data/information and their advanced applications. The papers are organized in the following topics: geometry and machine learning; divergences and computational information geometry; statistics, topology and shape spaces; geometry and mechanics; geometry, learning dynamics and thermodynamics; quantum information geometry; geometry and biological structures; geometry and applications.
Author : Stefan Bedbur,Udo Kamps
Publisher : Springer Nature
Page : 147 pages
File Size : 43,8 Mb
Release : 2021-10-07
Category : Mathematics
ISBN : 9783030819002
This book provides a concise introduction to exponential families. Parametric families of probability distributions and their properties are extensively studied in the literature on statistical modeling and inference. Exponential families of distributions comprise density functions of a particular form, which enables general assertions and leads to nice features. With a focus on parameter estimation and hypotheses testing, the text introduces the reader to distributional and statistical properties of multivariate and multiparameter exponential families along with a variety of detailed examples. The material is widely self-contained and written in a mathematical setting. It may serve both as a concise, mathematically rigorous course on exponential families in a systematic structure and as an introduction to Mathematical Statistics restricted to the use of exponential families.
Author : Neil S. Barnett
Publisher : Nova Publishers
Page : 244 pages
File Size : 52,5 Mb
Release : 2008
Category : Mathematics
ISBN : 1600219438
This is the first in a series of research monographs that focus on the research, development and use of inequalities in probability and statistics. All of the papers have been peer refereed and this first edition covers a range of topics that include both survey material of published work as well as new results appearing in print for the first time.
Author : Vlad Stefan Barbu,Nicolas Vergne
Publisher : John Wiley & Sons
Page : 288 pages
File Size : 47,6 Mb
Release : 2020-12-03
Category : Mathematics
ISBN : 9781786306036
This book is a collective volume authored by leading scientists in the field of stochastic modelling, associated statistical topics and corresponding applications. The main classes of stochastic processes for dependent data investigated throughout this book are Markov, semi-Markov, autoregressive and piecewise deterministic Markov models. The material is divided into three parts corresponding to: (i) Markov and semi-Markov processes, (ii) autoregressive processes and (iii) techniques based on divergence measures and entropies. A special attention is payed to applications in reliability, survival analysis and related fields.