Simplicity Inference And Modelling
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Simplicity Inference And Modelling
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Author : Arnold Zellner
language : en
Publisher: Cambridge University Press
Release Date : 2002-02-07
Simplicity Inference And Modelling written by Arnold Zellner and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002-02-07 with Business & Economics categories.
The idea that simplicity matters in science is as old as science itself, with the much cited example of Ockham's Razor, 'entia non sunt multiplicanda praeter necessitatem': entities are not to be multiplied beyond necessity. A problem with Ockham's razor is that nearly everybody seems to accept it, but few are able to define its exact meaning and to make it operational in a non-arbitrary way. Using a multidisciplinary perspective including philosophers, mathematicians, econometricians and economists, this 2002 monograph examines simplicity by asking six questions: what is meant by simplicity? How is simplicity measured? Is there an optimum trade-off between simplicity and goodness-of-fit? What is the relation between simplicity and empirical modelling? What is the relation between simplicity and prediction? What is the connection between simplicity and convenience? The book concludes with reflections on simplicity by Nobel Laureates in Economics.
Simplicity Inference And Modeling
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Author : Arnold Zellner
language : en
Publisher:
Release Date : 2001
Simplicity Inference And Modeling written by Arnold Zellner and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001 with Econometrics categories.
The idea that simplicity matters in science is as old as science itself, with the much cited example of Ockham's Razor, 'entia non sunt multiplicanda praeter necessitatem': entities are not to be multiplied beyond necessity. Using a multidisciplinary perspective this monograph asks 'What is meant by simplicity?'
Simplicity Complexity And Modelling
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Author : Mike Christie
language : en
Publisher: John Wiley & Sons
Release Date : 2011-10-19
Simplicity Complexity And Modelling written by Mike Christie and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-10-19 with Mathematics categories.
Several points of disagreement exist between different modelling traditions as to whether complex models are always better than simpler models, as to how to combine results from different models and how to propagate model uncertainty into forecasts. This book represents the result of collaboration between scientists from many disciplines to show how these conflicts can be resolved. Key Features: Introduces important concepts in modelling, outlining different traditions in the use of simple and complex modelling in statistics. Provides numerous case studies on complex modelling, such as climate change, flood risk and new drug development. Concentrates on varying models, including flood risk analysis models, the petrol industry forecasts and summarizes the evolution of water distribution systems. Written by experienced statisticians and engineers in order to facilitate communication between modellers in different disciplines. Provides a glossary giving terms commonly used in different modelling traditions. This book provides a much-needed reference guide to approaching statistical modelling. Scientists involved with modelling complex systems in areas such as climate change, flood prediction and prevention, financial market modelling and systems engineering will benefit from this book. It will also be a useful source of modelling case histories.
Foundations Of Info Metrics
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Author : Amos Golan
language : en
Publisher: Oxford University Press
Release Date : 2017-11-07
Foundations Of Info Metrics written by Amos Golan and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-11-07 with Business & Economics categories.
Info-metrics is the science of modeling, reasoning, and drawing inferences under conditions of noisy and insufficient information. It is at the intersection of information theory, statistical inference, and decision-making under uncertainty. It plays an important role in helping make informed decisions even when there is inadequate or incomplete information because it provides a framework to process available information with minimal reliance on assumptions that cannot be validated. In this pioneering book, Amos Golan, a leader in info-metrics, focuses on unifying information processing, modeling and inference within a single constrained optimization framework. Foundations of Info-Metrics provides an overview of modeling and inference, rather than a problem specific model, and progresses from the simple premise that information is often insufficient to provide a unique answer for decisions we wish to make. Each decision, or solution, is derived from the available input information along with a choice of inferential procedure. The book contains numerous multidisciplinary applications and case studies, which demonstrate the simplicity and generality of the framework in real world settings. Examples include initial diagnosis at an emergency room, optimal dose decisions, election forecasting, network and information aggregation, weather pattern analyses, portfolio allocation, strategy inference for interacting entities, incorporation of prior information, option pricing, and modeling an interacting social system. Graphical representations illustrate how results can be visualized while exercises and problem sets facilitate extensions. This book is this designed to be accessible for researchers, graduate students, and practitioners across the disciplines.
Simplicity Scientific Inference And Econometric Modelling
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Author : Hugo A. Keuzenkamp
language : en
Publisher:
Release Date : 1994
Simplicity Scientific Inference And Econometric Modelling written by Hugo A. Keuzenkamp and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994 with Simplicity categories.
Model Free Prediction And Regression
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Author : Dimitris N. Politis
language : en
Publisher: Springer
Release Date : 2015-11-13
Model Free Prediction And Regression written by Dimitris N. Politis and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-11-13 with Mathematics categories.
The Model-Free Prediction Principle expounded upon in this monograph is based on the simple notion of transforming a complex dataset to one that is easier to work with, e.g., i.i.d. or Gaussian. As such, it restores the emphasis on observable quantities, i.e., current and future data, as opposed to unobservable model parameters and estimates thereof, and yields optimal predictors in diverse settings such as regression and time series. Furthermore, the Model-Free Bootstrap takes us beyond point prediction in order to construct frequentist prediction intervals without resort to unrealistic assumptions such as normality. Prediction has been traditionally approached via a model-based paradigm, i.e., (a) fit a model to the data at hand, and (b) use the fitted model to extrapolate/predict future data. Due to both mathematical and computational constraints, 20th century statistical practice focused mostly on parametric models. Fortunately, with the advent of widely accessible powerful computing in the late 1970s, computer-intensive methods such as the bootstrap and cross-validation freed practitioners from the limitations of parametric models, and paved the way towards the `big data' era of the 21st century. Nonetheless, there is a further step one may take, i.e., going beyond even nonparametric models; this is where the Model-Free Prediction Principle is useful. Interestingly, being able to predict a response variable Y associated with a regressor variable X taking on any possible value seems to inadvertently also achieve the main goal of modeling, i.e., trying to describe how Y depends on X. Hence, as prediction can be treated as a by-product of model-fitting, key estimation problems can be addressed as a by-product of being able to perform prediction. In other words, a practitioner can use Model-Free Prediction ideas in order to additionally obtain point estimates and confidence intervals for relevant parameters leading to an alternative, transformation-based approach to statistical inference.
Inferential Models
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Author : Ryan Martin
language : en
Publisher: CRC Press
Release Date : 2015-09-25
Inferential Models written by Ryan Martin and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-09-25 with Mathematics categories.
A New Approach to Sound Statistical ReasoningInferential Models: Reasoning with Uncertainty introduces the authors' recently developed approach to inference: the inferential model (IM) framework. This logical framework for exact probabilistic inference does not require the user to input prior information. The authors show how an IM produces meaning
The Structure Of Scientific Inference
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Author : Mary B. Hesse
language : en
Publisher: Univ of California Press
Release Date : 1974
The Structure Of Scientific Inference written by Mary B. Hesse and has been published by Univ of California Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1974 with Science categories.
Model Selection And Inference
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Author : Kenneth P. Burnham
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-11-11
Model Selection And Inference written by Kenneth P. Burnham and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-11-11 with Mathematics categories.
Statisticians and applied scientists must often select a model to fit empirical data. This book discusses the philosophy and strategy of selecting such a model using the information theory approach pioneered by Hirotugu Akaike. This approach focuses critical attention on a priori modeling and the selection of a good approximating model that best represents the inference supported by the data. The book includes practical applications in biology and environmental science.
Models And Inferences In Science
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Author : Emiliano Ippoliti
language : en
Publisher: Springer
Release Date : 2018-03-30
Models And Inferences In Science written by Emiliano Ippoliti and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-03-30 with Science categories.
The book answers long-standing questions on scientific modeling and inference across multiple perspectives and disciplines, including logic, mathematics, physics and medicine. The different chapters cover a variety of issues, such as the role models play in scientific practice; the way science shapes our concept of models; ways of modeling the pursuit of scientific knowledge; the relationship between our concept of models and our concept of science. The book also discusses models and scientific explanations; models in the semantic view of theories; the applicability of mathematical models to the real world and their effectiveness; the links between models and inferences; and models as a means for acquiring new knowledge. It analyzes different examples of models in physics, biology, mathematics and engineering. Written for researchers and graduate students, it provides a cross-disciplinary reference guide to the notion and the use of models and inferences in science.
