The Skew-Normal and Related Families

The Skew-Normal and Related Families PDF Author: Adelchi Azzalini
Publisher: Cambridge University Press
ISBN: 1107029279
Category : Business & Economics
Languages : en
Pages : 271

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Book Description
The standard resource for statisticians and applied researchers. Accessible to the wide range of researchers who use statistical modelling techniques.

The Skew-Normal and Related Families

The Skew-Normal and Related Families PDF Author: Adelchi Azzalini
Publisher: Cambridge University Press
ISBN: 1107029279
Category : Business & Economics
Languages : en
Pages : 271

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Book Description
The standard resource for statisticians and applied researchers. Accessible to the wide range of researchers who use statistical modelling techniques.

Skew-Elliptical Distributions and Their Applications

Skew-Elliptical Distributions and Their Applications PDF Author: Marc G. Genton
Publisher: CRC Press
ISBN: 0203492005
Category : Mathematics
Languages : en
Pages : 420

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Book Description
This book reviews the state-of-the-art advances in skew-elliptical distributions and provides many new developments in a single volume, collecting theoretical results and applications previously scattered throughout the literature. The main goal of this research area is to develop flexible parametric classes of distributions beyond the classical no

Asymmetric Dependence in Finance

Asymmetric Dependence in Finance PDF Author: Jamie Alcock
Publisher: John Wiley & Sons
ISBN: 1119289017
Category : Business & Economics
Languages : en
Pages : 312

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Book Description
Avoid downturn vulnerability by managing correlation dependency Asymmetric Dependence in Finance examines the risks and benefits of asset correlation, and provides effective strategies for more profitable portfolio management. Beginning with a thorough explanation of the extent and nature of asymmetric dependence in the financial markets, this book delves into the practical measures fund managers and investors can implement to boost fund performance. From managing asymmetric dependence using Copulas, to mitigating asymmetric dependence risk in real estate, credit and CTA markets, the discussion presents a coherent survey of the state-of-the-art tools available for measuring and managing this difficult but critical issue. Many funds suffered significant losses during recent downturns, despite having a seemingly well-diversified portfolio. Empirical evidence shows that the relation between assets is much richer than previously thought, and correlation between returns is dependent on the state of the market; this book explains this asymmetric dependence and provides authoritative guidance on mitigating the risks. Examine an options-based approach to limiting your portfolio's downside risk Manage asymmetric dependence in larger portfolios and alternate asset classes Get up to speed on alternative portfolio performance management methods Improve fund performance by applying appropriate models and quantitative techniques Correlations between assets increase markedly during market downturns, leading to diversification failure at the very moment it is needed most. The 2008 Global Financial Crisis and the 2006 hedge-fund crisis provide vivid examples, and many investors still bear the scars of heavy losses from their well-managed, well-diversified portfolios. Asymmetric Dependence in Finance shows you what went wrong, and how it can be corrected and managed before the next big threat using the latest methods and models from leading research in quantitative finance.

Symmetric Multivariate and Related Distributions

Symmetric Multivariate and Related Distributions PDF Author: Kai Wang Fang
Publisher: CRC Press
ISBN: 1351093940
Category : Mathematics
Languages : en
Pages : 165

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Book Description
Since the publication of the by now classical Johnson and Kotz Continuous Multivariate Distributions (Wiley, 1972) there have been substantial developments in multivariate distribution theory especially in the area of non-normal symmetric multivariate distributions. The book by Fang, Kotz and Ng summarizes these developments in a manner which is accessible to a reader with only limited background (advanced real-analysis calculus, linear algebra and elementary matrix calculus). Many of the results in this field are due to Kai-Tai Fang and his associates and appeared in Chinese publications only. A thorough literature search was conducted and the book represents the latest work - as of 1988 - in this rapidly developing field of multivariate distributions. The authors are experts in statistical distribution theory.

The Multivariate Normal Distribution

The Multivariate Normal Distribution PDF Author: Y.L. Tong
Publisher: Springer Science & Business Media
ISBN: 1461396557
Category : Business & Economics
Languages : en
Pages : 281

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Book Description
The multivariate normal distribution has played a predominant role in the historical development of statistical theory, and has made its appearance in various areas of applications. Although many of the results concerning the multivariate normal distribution are classical, there are important new results which have been reported recently in the literature but cannot be found in most books on multivariate analysis. These results are often obtained by showing that the multivariate normal density function belongs to certain large families of density functions. Thus, useful properties of such families immedi ately hold for the multivariate normal distribution. This book attempts to provide a comprehensive and coherent treatment of the classical and new results related to the multivariate normal distribution. The material is organized in a unified modern approach, and the main themes are dependence, probability inequalities, and their roles in theory and applica tions. Some general properties of a multivariate normal density function are discussed, and results that follow from these properties are reviewed exten sively. The coverage is, to some extent, a matter of taste and is not intended to be exhaustive, thus more attention is focused on a systematic presentation of results rather than on a complete listing of them.

Normal and Student ́s t Distributions and Their Applications

Normal and Student ́s t Distributions and Their Applications PDF Author: Mohammad Ahsanullah
Publisher: Springer Science & Business Media
ISBN: 9462390614
Category : Mathematics
Languages : en
Pages : 163

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Book Description
The most important properties of normal and Student t-distributions are presented. A number of applications of these properties are demonstrated. New related results dealing with the distributions of the sum, product and ratio of the independent normal and Student distributions are presented. The materials will be useful to the advanced undergraduate and graduate students and practitioners in the various fields of science and engineering.

An Introduction to Regression Graphics

An Introduction to Regression Graphics PDF Author: R. Dennis Cook
Publisher: John Wiley & Sons
ISBN: 0470317701
Category : Mathematics
Languages : en
Pages : 282

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Book Description
Covers the use of dynamic and interactive computer graphics in linear regression analysis, focusing on analytical graphics. Features new techniques like plot rotation. The authors have composed their own regression code, using Xlisp-Stat language called R-code, which is a nearly complete system for linear regression analysis and can be utilized as the main computer program in a linear regression course. The accompanying disks, for both Macintosh and Windows computers, contain the R-code and Xlisp-Stat. An Instructor's Manual presenting detailed solutions to all the problems in the book is available upon request from the Wiley editorial department.

Statistical Inference Based on the likelihood

Statistical Inference Based on the likelihood PDF Author: Adelchi Azzalini
Publisher: Routledge
ISBN: 1351414461
Category : Mathematics
Languages : en
Pages : 356

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Book Description
The Likelihood plays a key role in both introducing general notions of statistical theory, and in developing specific methods. This book introduces likelihood-based statistical theory and related methods from a classical viewpoint, and demonstrates how the main body of currently used statistical techniques can be generated from a few key concepts, in particular the likelihood. Focusing on those methods, which have both a solid theoretical background and practical relevance, the author gives formal justification of the methods used and provides numerical examples with real data.

Robustness in Econometrics

Robustness in Econometrics PDF Author: Vladik Kreinovich
Publisher: Springer
ISBN: 3319507427
Category : Technology & Engineering
Languages : en
Pages : 693

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Book Description
This book presents recent research on robustness in econometrics. Robust data processing techniques – i.e., techniques that yield results minimally affected by outliers – and their applications to real-life economic and financial situations are the main focus of this book. The book also discusses applications of more traditional statistical techniques to econometric problems. Econometrics is a branch of economics that uses mathematical (especially statistical) methods to analyze economic systems, to forecast economic and financial dynamics, and to develop strategies for achieving desirable economic performance. In day-by-day data, we often encounter outliers that do not reflect the long-term economic trends, e.g., unexpected and abrupt fluctuations. As such, it is important to develop robust data processing techniques that can accommodate these fluctuations.

Graphical Models with R

Graphical Models with R PDF Author: Søren Højsgaard
Publisher: Springer Science & Business Media
ISBN: 146142299X
Category : Mathematics
Languages : en
Pages : 187

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Book Description
Graphical models in their modern form have been around since the late 1970s and appear today in many areas of the sciences. Along with the ongoing developments of graphical models, a number of different graphical modeling software programs have been written over the years. In recent years many of these software developments have taken place within the R community, either in the form of new packages or by providing an R interface to existing software. This book attempts to give the reader a gentle introduction to graphical modeling using R and the main features of some of these packages. In addition, the book provides examples of how more advanced aspects of graphical modeling can be represented and handled within R. Topics covered in the seven chapters include graphical models for contingency tables, Gaussian and mixed graphical models, Bayesian networks and modeling high dimensional data.