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Author: David Fletcher
Publisher: Springer
ISBN: 3662585413
Category : Mathematics
Languages : en
Pages : 112
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Book Description
This book provides a concise and accessible overview of model averaging, with a focus on applications. Model averaging is a common means of allowing for model uncertainty when analysing data, and has been used in a wide range of application areas, such as ecology, econometrics, meteorology and pharmacology. The book presents an overview of the methods developed in this area, illustrating many of them with examples from the life sciences involving real-world data. It also includes an extensive list of references and suggestions for further research. Further, it clearly demonstrates the links between the methods developed in statistics, econometrics and machine learning, as well as the connection between the Bayesian and frequentist approaches to model averaging. The book appeals to statisticians and scientists interested in what methods are available, how they differ and what is known about their properties. It is assumed that readers are familiar with the basic concepts of statistical theory and modelling, including probability, likelihood and generalized linear models.
Author: David Fletcher
Publisher: Springer
ISBN: 3662585413
Category : Mathematics
Languages : en
Pages : 112
Get Book Here
Book Description
This book provides a concise and accessible overview of model averaging, with a focus on applications. Model averaging is a common means of allowing for model uncertainty when analysing data, and has been used in a wide range of application areas, such as ecology, econometrics, meteorology and pharmacology. The book presents an overview of the methods developed in this area, illustrating many of them with examples from the life sciences involving real-world data. It also includes an extensive list of references and suggestions for further research. Further, it clearly demonstrates the links between the methods developed in statistics, econometrics and machine learning, as well as the connection between the Bayesian and frequentist approaches to model averaging. The book appeals to statisticians and scientists interested in what methods are available, how they differ and what is known about their properties. It is assumed that readers are familiar with the basic concepts of statistical theory and modelling, including probability, likelihood and generalized linear models.
Author: Gerda Claeskens
Publisher:
ISBN: 9780521852258
Category : Mathematics
Languages : en
Pages : 312
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Book Description
First book to synthesize the research and practice from the active field of model selection.
Author: Robert N. Rodriguez
Publisher: SAS Institute
ISBN: 1951684001
Category : Computers
Languages : en
Pages : 464
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Book Description
Advance your skills in building predictive models with SAS! Building Regression Models with SAS: A Guide for Data Scientists teaches data scientists, statisticians, and other analysts who use SAS to train regression models for prediction with large, complex data. Each chapter focuses on a particular model and includes a high-level overview, followed by basic concepts, essential syntax, and examples using new procedures in both SAS/STAT and SAS Viya. By emphasizing introductory examples and interpretation of output, this book provides readers with a clear understanding of how to build the following types of models: general linear models quantile regression models logistic regression models generalized linear models generalized additive models proportional hazards regression models tree models models based on multivariate adaptive regression splines Building Regression Models with SAS is an essential guide to learning about a variety of models that provide interpretability as well as predictive performance.
Author: J. M. Bernardo
Publisher: Oxford University Press
ISBN: 9780198504856
Category : Business & Economics
Languages : en
Pages : 886
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Book Description
Bayesian statistics is a dynamic and fast-growing area of statistical research and the Valencia International Meetings provide the main forum for discussion. These resulting proceedings form an up-to-date collection of research.
Author: Peter B. Dixon
Publisher: Newnes
ISBN: 044462631X
Category : Business & Economics
Languages : en
Pages : 1886
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Book Description
Top scholars synthesize and analyze scholarship on this widely used tool of policy analysis in 27 articles, setting forth its accomplishments, difficulties, and means of implementation. Though CGE modeling does not play a prominent role in top U.S. graduate schools, it is employed universally in the development of economic policy. This collection is particularly important because it presents a history of modeling applications and examines competing points of view. - Presents coherent summaries of CGE theories that inform major model types - Covers the construction of CGE databases, model solving, and computer-assisted interpretation of results - Shows how CGE modeling has made a contribution to economic policy
Author: David R. Anderson
Publisher: Springer Science & Business Media
ISBN: 0387740759
Category : Science
Languages : en
Pages : 203
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Book Description
This textbook introduces a science philosophy called "information theoretic" based on Kullback-Leibler information theory. It focuses on a science philosophy based on "multiple working hypotheses" and statistical models to represent them. The text is written for people new to the information-theoretic approaches to statistical inference, whether graduate students, post-docs, or professionals. Readers are however expected to have a background in general statistical principles, regression analysis, and some exposure to likelihood methods. This is not an elementary text as it assumes reasonable competence in modeling and parameter estimation.
Author: Qiang Qiang Yang
Publisher: Springer Nature
ISBN: 3031015851
Category : Computers
Languages : en
Pages : 189
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Book Description
How is it possible to allow multiple data owners to collaboratively train and use a shared prediction model while keeping all the local training data private? Traditional machine learning approaches need to combine all data at one location, typically a data center, which may very well violate the laws on user privacy and data confidentiality. Today, many parts of the world demand that technology companies treat user data carefully according to user-privacy laws. The European Union's General Data Protection Regulation (GDPR) is a prime example. In this book, we describe how federated machine learning addresses this problem with novel solutions combining distributed machine learning, cryptography and security, and incentive mechanism design based on economic principles and game theory. We explain different types of privacy-preserving machine learning solutions and their technological backgrounds, and highlight some representative practical use cases. We show how federated learning can become the foundation of next-generation machine learning that caters to technological and societal needs for responsible AI development and application.
Author: Larkin Powell
Publisher: Lulu.com
ISBN: 1329061519
Category : Science
Languages : en
Pages : 258
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Book Description
This book is a simple introduction to the logic behind analyses and sampling design for mark-recapture and survey efforts. With a focus on the early user and beginner, the book explains the complicated formulas and statistics that can be effectively used around the world in support of conservation efforts.
Author: A. Berkelaar
Publisher: Springer
ISBN: 0230251293
Category : Political Science
Languages : en
Pages : 401
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Book Description
This edited volume contains essential readings for financial analysts and market practitioners working at Central Banks and Sovereign Wealth Funds. It presents the reader with state-of-the-art methods that are directly implementable, and industry 'best-practices' as followed by leading institutions in their field.
Author: Yiu-Kuen Tse
Publisher: MDPI
ISBN: 3039216260
Category : Business & Economics
Languages : en
Pages : 136
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Book Description
Financial econometrics has developed into a very fruitful and vibrant research area in the last two decades. The availability of good data promotes research in this area, specially aided by online data and high-frequency data. These two characteristics of financial data also create challenges for researchers that are different from classical macro-econometric and micro-econometric problems. This Special Issue is dedicated to research topics that are relevant for analyzing financial data. We have gathered six articles under this theme.
