Information Criteria And Statistical Modeling

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Information Criteria and Statistical Modeling

Sadanori Konishi,Genshiro Kitagawa
Information Criteria and Statistical Modeling Book PDF

Author : Sadanori Konishi,Genshiro Kitagawa
Publisher : Springer Science & Business Media
Page : 282 pages
File Size : 50,7 Mb
Release : 2008
Category : Business & Economics
ISBN : 9780387718866

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Information Criteria and Statistical Modeling by Sadanori Konishi,Genshiro Kitagawa Pdf

Statistical modeling is a critical tool in scientific research. This book provides comprehensive explanations of the concepts and philosophy of statistical modeling, together with a wide range of practical and numerical examples. The authors expect this work to be of great value not just to statisticians but also to researchers and practitioners in various fields of research such as information science, computer science, engineering, bioinformatics, economics, marketing and environmental science. It’s a crucial area of study, as statistical models are used to understand phenomena with uncertainty and to determine the structure of complex systems. They’re also used to control such systems, as well as to make reliable predictions in various natural and social science fields.

Bayesian Model Selection and Statistical Modeling

Tomohiro Ando
Bayesian Model Selection and Statistical Modeling Book PDF

Author : Tomohiro Ando
Publisher : CRC Press
Page : 300 pages
File Size : 52,5 Mb
Release : 2010-05-27
Category : Mathematics
ISBN : 1439836159

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Bayesian Model Selection and Statistical Modeling by Tomohiro Ando Pdf

Along with many practical applications, Bayesian Model Selection and Statistical Modeling presents an array of Bayesian inference and model selection procedures. It thoroughly explains the concepts, illustrates the derivations of various Bayesian model selection criteria through examples, and provides R code for implementation. The author shows how to implement a variety of Bayesian inference using R and sampling methods, such as Markov chain Monte Carlo. He covers the different types of simulation-based Bayesian model selection criteria, including the numerical calculation of Bayes factors, the Bayesian predictive information criterion, and the deviance information criterion. He also provides a theoretical basis for the analysis of these criteria. In addition, the author discusses how Bayesian model averaging can simultaneously treat both model and parameter uncertainties. Selecting and constructing the appropriate statistical model significantly affect the quality of results in decision making, forecasting, stochastic structure explorations, and other problems. Helping you choose the right Bayesian model, this book focuses on the framework for Bayesian model selection and includes practical examples of model selection criteria.

Statistical Rethinking

Richard McElreath
Statistical Rethinking Book PDF

Author : Richard McElreath
Publisher : CRC Press
Page : 488 pages
File Size : 46,8 Mb
Release : 2018-01-03
Category : Mathematics
ISBN : 9781315362618

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Statistical Rethinking by Richard McElreath Pdf

Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds readers’ knowledge of and confidence in statistical modeling. Reflecting the need for even minor programming in today’s model-based statistics, the book pushes readers to perform step-by-step calculations that are usually automated. This unique computational approach ensures that readers understand enough of the details to make reasonable choices and interpretations in their own modeling work. The text presents generalized linear multilevel models from a Bayesian perspective, relying on a simple logical interpretation of Bayesian probability and maximum entropy. It covers from the basics of regression to multilevel models. The author also discusses measurement error, missing data, and Gaussian process models for spatial and network autocorrelation. By using complete R code examples throughout, this book provides a practical foundation for performing statistical inference. Designed for both PhD students and seasoned professionals in the natural and social sciences, it prepares them for more advanced or specialized statistical modeling. Web Resource The book is accompanied by an R package (rethinking) that is available on the author’s website and GitHub. The two core functions (map and map2stan) of this package allow a variety of statistical models to be constructed from standard model formulas.

From Data to Model

Jan C. Willems
From Data to Model Book PDF

Author : Jan C. Willems
Publisher : Springer Science & Business Media
Page : 254 pages
File Size : 44,7 Mb
Release : 2012-12-06
Category : Business & Economics
ISBN : 9783642750076

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From Data to Model by Jan C. Willems Pdf

The problem of obtaining dynamical models directly from an observed time-series occurs in many fields of application. There are a number of possible approaches to this problem. In this volume a number of such points of view are exposed: the statistical time series approach, a theory of guaranted performance, and finally a deterministic approximation approach. This volume is an out-growth of a number of get-togethers sponsered by the Systems and Decision Sciences group of the International Institute of Applied Systems Analysis (IIASA) in Laxenburg, Austria. The hospitality and support of this organization is gratefully acknowledged. Jan Willems Groningen, the Netherlands May 1989 TABLE OF CONTENTS Linear System Identification- A Survey page 1 M. Deistler A Tutorial on Hankel-Norm Approximation 26 K. Glover A Deterministic Approach to Approximate Modelling 49 C. Heij and J. C. Willems Identification - a Theory of Guaranteed Estimates 135 A. B. Kurzhanski Statistical Aspects of Model Selection 215 R. Shibata Index 241 Addresses of Authors 246 LINEAR SYSTEM IDENTIFICATION· A SURVEY M. DEISTLER Abstract In this paper we give an introductory survey on the theory of identification of (in general MIMO) linear systems from (discrete) time series data. The main parts are: Structure theory for linear systems, asymptotic properties of maximum likelihood type estimators, estimation of the dynamic specification by methods based on information criteria and finally, extensions and alternative approaches such as identification of unstable systems and errors-in-variables. Keywords Linear systems, parametrization, maximum likelihood estimation, information criteria, errors-in-variables.

Linear Models in Statistics

Alvin C. Rencher,G. Bruce Schaalje
Linear Models in Statistics Book PDF

Author : Alvin C. Rencher,G. Bruce Schaalje
Publisher : John Wiley & Sons
Page : 690 pages
File Size : 42,7 Mb
Release : 2008-01-07
Category : Mathematics
ISBN : 9780470192603

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Linear Models in Statistics by Alvin C. Rencher,G. Bruce Schaalje Pdf

The essential introduction to the theory and application of linear models—now in a valuable new edition Since most advanced statistical tools are generalizations of the linear model, it is neces-sary to first master the linear model in order to move forward to more advanced concepts. The linear model remains the main tool of the applied statistician and is central to the training of any statistician regardless of whether the focus is applied or theoretical. This completely revised and updated new edition successfully develops the basic theory of linear models for regression, analysis of variance, analysis of covariance, and linear mixed models. Recent advances in the methodology related to linear mixed models, generalized linear models, and the Bayesian linear model are also addressed. Linear Models in Statistics, Second Edition includes full coverage of advanced topics, such as mixed and generalized linear models, Bayesian linear models, two-way models with empty cells, geometry of least squares, vector-matrix calculus, simultaneous inference, and logistic and nonlinear regression. Algebraic, geometrical, frequentist, and Bayesian approaches to both the inference of linear models and the analysis of variance are also illustrated. Through the expansion of relevant material and the inclusion of the latest technological developments in the field, this book provides readers with the theoretical foundation to correctly interpret computer software output as well as effectively use, customize, and understand linear models. This modern Second Edition features: New chapters on Bayesian linear models as well as random and mixed linear models Expanded discussion of two-way models with empty cells Additional sections on the geometry of least squares Updated coverage of simultaneous inference The book is complemented with easy-to-read proofs, real data sets, and an extensive bibliography. A thorough review of the requisite matrix algebra has been addedfor transitional purposes, and numerous theoretical and applied problems have been incorporated with selected answers provided at the end of the book. A related Web site includes additional data sets and SAS® code for all numerical examples. Linear Model in Statistics, Second Edition is a must-have book for courses in statistics, biostatistics, and mathematics at the upper-undergraduate and graduate levels. It is also an invaluable reference for researchers who need to gain a better understanding of regression and analysis of variance.

Model Selection and Multimodel Inference

Kenneth P. Burnham,David R. Anderson
Model Selection and Multimodel Inference Book PDF

Author : Kenneth P. Burnham,David R. Anderson
Publisher : Springer Science & Business Media
Page : 512 pages
File Size : 46,8 Mb
Release : 2007-05-28
Category : Mathematics
ISBN : 9780387224565

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Model Selection and Multimodel Inference by Kenneth P. Burnham,David R. Anderson Pdf

A unique and comprehensive text on the philosophy of model-based data analysis and strategy for the analysis of empirical data. The book introduces information theoretic approaches and focuses critical attention on a priori modeling and the selection of a good approximating model that best represents the inference supported by the data. It contains several new approaches to estimating model selection uncertainty and incorporating selection uncertainty into estimates of precision. An array of examples is given to illustrate various technical issues. The text has been written for biologists and statisticians using models for making inferences from empirical data.

Econometrics for Financial Applications

Ly H. Anh,Le Si Dong,Vladik Kreinovich,Nguyen Ngoc Thach
Econometrics for Financial Applications Book PDF

Author : Ly H. Anh,Le Si Dong,Vladik Kreinovich,Nguyen Ngoc Thach
Publisher : Springer
Page : 1081 pages
File Size : 49,8 Mb
Release : 2017-12-18
Category : Technology & Engineering
ISBN : 9783319731506

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Econometrics for Financial Applications by Ly H. Anh,Le Si Dong,Vladik Kreinovich,Nguyen Ngoc Thach Pdf

This book addresses both theoretical developments in and practical applications of econometric techniques to finance-related problems. It includes selected edited outcomes of the International Econometric Conference of Vietnam (ECONVN2018), held at Banking University, Ho Chi Minh City, Vietnam on January 15-16, 2018. 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. An extremely important part of economics is finances: a financial crisis can bring the whole economy to a standstill and, vice versa, a smart financial policy can dramatically boost economic development. It is therefore crucial to be able to apply mathematical techniques of econometrics to financial problems. Such applications are a growing field, with many interesting results – and an even larger number of challenges and open problems.

Model Based Inference in the Life Sciences

David R. Anderson
Model Based Inference in the Life Sciences Book PDF

Author : David R. Anderson
Publisher : Springer Science & Business Media
Page : 203 pages
File Size : 43,9 Mb
Release : 2007-12-22
Category : Science
ISBN : 9780387740751

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Model Based Inference in the Life Sciences by David R. Anderson Pdf

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.

Applied Linear Statistical Models

Michael H. Kutner
Applied Linear Statistical Models Book PDF

Author : Michael H. Kutner
Publisher : McGraw-Hill Education
Page : 1396 pages
File Size : 46,9 Mb
Release : 2005
Category : Analysis of variance
ISBN : 0071122214

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Applied Linear Statistical Models by Michael H. Kutner Pdf

Applied Linear Statistical Models 5e is the long established leading authoritative text and reference on statistical modeling. For students in most any discipline where statistical analysis or interpretation is used, ALSM serves as the standard work. The text includes brief introductory and review material, and then proceeds through regression and modeling for the first half, and through ANOVA and Experimental Design in the second half. All topics are presented in a precise and clear style supported with solved examples, numbered formulae, graphic illustrations, and "Notes" to provide depth and statistical accuracy and precision. Applications used within the text and the hallmark problems, exercises, and projects are drawn from virtually all disciplines and fields providing motivation for students in virtually any college. The Fifth edition provides an increased use of computing and graphical analysis throughout, without sacrificing concepts or rigor. In general, the 5e uses larger data sets in examples and exercises, and where methods can be automated within software without loss of understanding, it is so done.

Model Selection and Inference

Kenneth P. Burnham,David R. Anderson
Model Selection and Inference Book PDF

Author : Kenneth P. Burnham,David R. Anderson
Publisher : Springer Science & Business Media
Page : 373 pages
File Size : 51,5 Mb
Release : 2013-11-11
Category : Mathematics
ISBN : 9781475729177

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Model Selection and Inference by Kenneth P. Burnham,David R. Anderson Pdf

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.

Multivariate Statistical Modeling and Data Analysis

H. Bozdogan,Arjun K. Gupta
Multivariate Statistical Modeling and Data Analysis Book PDF

Author : H. Bozdogan,Arjun K. Gupta
Publisher : Springer Science & Business Media
Page : 193 pages
File Size : 47,7 Mb
Release : 2012-12-06
Category : Mathematics
ISBN : 9789400939776

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Multivariate Statistical Modeling and Data Analysis by H. Bozdogan,Arjun K. Gupta Pdf

This volume contains the Proceedings of the Advanced Symposium on Multivariate Modeling and Data Analysis held at the 64th Annual Heeting of the Virginia Academy of Sciences (VAS)--American Statistical Association's Vir ginia Chapter at James Madison University in Harrisonburg. Virginia during Hay 15-16. 1986. This symposium was sponsored by financial support from the Center for Advanced Studies at the University of Virginia to promote new and modern information-theoretic statist ical modeling procedures and to blend these new techniques within the classical theory. Multivariate statistical analysis has come a long way and currently it is in an evolutionary stage in the era of high-speed computation and computer technology. The Advanced Symposium was the first to address the new innovative approaches in multi variate analysis to develop modern analytical and yet practical procedures to meet the needs of researchers and the societal need of statistics. vii viii PREFACE Papers presented at the Symposium by e1l11lJinent researchers in the field were geared not Just for specialists in statistics, but an attempt has been made to achieve a well balanced and uniform coverage of different areas in multi variate modeling and data analysis. The areas covered included topics in the analysis of repeated measurements, cluster analysis, discriminant analysis, canonical cor relations, distribution theory and testing, bivariate densi ty estimation, factor analysis, principle component analysis, multidimensional scaling, multivariate linear models, nonparametric regression, etc.

Regression Modeling Strategies

Frank E. Harrell
Regression Modeling Strategies Book PDF

Author : Frank E. Harrell
Publisher : Springer Science & Business Media
Page : 583 pages
File Size : 54,8 Mb
Release : 2013-03-09
Category : Mathematics
ISBN : 9781475734621

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Regression Modeling Strategies by Frank E. Harrell Pdf

Many texts are excellent sources of knowledge about individual statistical tools, but the art of data analysis is about choosing and using multiple tools. Instead of presenting isolated techniques, this text emphasizes problem solving strategies that address the many issues arising when developing multivariable models using real data and not standard textbook examples. It includes imputation methods for dealing with missing data effectively, methods for dealing with nonlinear relationships and for making the estimation of transformations a formal part of the modeling process, methods for dealing with "too many variables to analyze and not enough observations," and powerful model validation techniques based on the bootstrap. This text realistically deals with model uncertainty and its effects on inference to achieve "safe data mining".

Fundamentals of Clinical Data Science

Pieter Kubben,Michel Dumontier,Andre Dekker
Fundamentals of Clinical Data Science Book PDF

Author : Pieter Kubben,Michel Dumontier,Andre Dekker
Publisher : Springer
Page : 219 pages
File Size : 46,9 Mb
Release : 2018-12-21
Category : Medical
ISBN : 9783319997131

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Fundamentals of Clinical Data Science by Pieter Kubben,Michel Dumontier,Andre Dekker Pdf

This open access book comprehensively covers the fundamentals of clinical data science, focusing on data collection, modelling and clinical applications. Topics covered in the first section on data collection include: data sources, data at scale (big data), data stewardship (FAIR data) and related privacy concerns. Aspects of predictive modelling using techniques such as classification, regression or clustering, and prediction model validation will be covered in the second section. The third section covers aspects of (mobile) clinical decision support systems, operational excellence and value-based healthcare. Fundamentals of Clinical Data Science is an essential resource for healthcare professionals and IT consultants intending to develop and refine their skills in personalized medicine, using solutions based on large datasets from electronic health records or telemonitoring programmes. The book’s promise is “no math, no code”and will explain the topics in a style that is optimized for a healthcare audience.

Bayesian Data Analysis

Andrew Gelman,John B. Carlin,Hal S. Stern,David B. Dunson,Aki Vehtari,Donald B. Rubin
Bayesian Data Analysis Book PDF

Author : Andrew Gelman,John B. Carlin,Hal S. Stern,David B. Dunson,Aki Vehtari,Donald B. Rubin
Publisher : CRC Press
Page : 663 pages
File Size : 53,9 Mb
Release : 2013-11-27
Category : Mathematics
ISBN : 9781439898208

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Bayesian Data Analysis by Andrew Gelman,John B. Carlin,Hal S. Stern,David B. Dunson,Aki Vehtari,Donald B. Rubin Pdf

Winner of the 2016 De Groot Prize from the International Society for Bayesian AnalysisNow in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied

Handbook of Probabilistic Models

Pijush Samui,Dieu Tien Bui,Subrata Chakraborty,Ravinesh C. Deo
Handbook of Probabilistic Models Book PDF

Author : Pijush Samui,Dieu Tien Bui,Subrata Chakraborty,Ravinesh C. Deo
Publisher : Butterworth-Heinemann
Page : 590 pages
File Size : 54,9 Mb
Release : 2019-10-05
Category : Computers
ISBN : 9780128165461

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Handbook of Probabilistic Models by Pijush Samui,Dieu Tien Bui,Subrata Chakraborty,Ravinesh C. Deo Pdf

Handbook of Probabilistic Models carefully examines the application of advanced probabilistic models in conventional engineering fields. In this comprehensive handbook, practitioners, researchers and scientists will find detailed explanations of technical concepts, applications of the proposed methods, and the respective scientific approaches needed to solve the problem. This book provides an interdisciplinary approach that creates advanced probabilistic models for engineering fields, ranging from conventional fields of mechanical engineering and civil engineering, to electronics, electrical, earth sciences, climate, agriculture, water resource, mathematical sciences and computer sciences. Specific topics covered include minimax probability machine regression, stochastic finite element method, relevance vector machine, logistic regression, Monte Carlo simulations, random matrix, Gaussian process regression, Kalman filter, stochastic optimization, maximum likelihood, Bayesian inference, Bayesian update, kriging, copula-statistical models, and more. Explains the application of advanced probabilistic models encompassing multidisciplinary research Applies probabilistic modeling to emerging areas in engineering Provides an interdisciplinary approach to probabilistic models and their applications, thus solving a wide range of practical problems