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Título : An Introduction to Copulas Tipo de documento: documento electrónico Autores: Nelsen, Roger B ; SpringerLink (Online service) Editorial: New York, NY : Springer New York Fecha de publicación: 2006 Colección: Springer Series in Statistics, ISSN 0172-7397 Número de páginas: XIV, 272 p Il.: online resource ISBN/ISSN/DL: 978-0-387-28678-5 Idioma : Inglés (eng) Palabras clave: Mathematics Computer simulation Economics, Mathematical Probabilities Statistics Probability Theory and Stochastic Processes Statistical Methods for Business/Economics/Mathematical Finance/Insurance Quantitative Finance Simulation Modeling Clasificación: 51 Matemáticas Resumen: Copulas are functions that join multivariate distribution functions to their one-dimensional margins. The study of copulas and their role in statistics is a new but vigorously growing field. In this book the student or practitioner of statistics and probability will find discussions of the fundamental properties of copulas and some of their primary applications. The applications include the study of dependence and measures of association, and the construction of families of bivariate distributions. With 116 examples, 54 figures, and 167 exercises, this book is suitable as a text or for self-study. The only prerequisite is an upper level undergraduate course in probability and mathematical statistics, although some familiarity with nonparametric statistics would be useful. Knowledge of measure-theoretic probability is not required. The revised second edition includes new sections on extreme value copulas, tail dependence, and quasi-copulas. Roger B. Nelsen is Professor of Mathematics at Lewis & Clark College in Portland, Oregon. He is also the author of Proofs Without Words: Exercises in Visual Thinking and Proofs Without Words II: More Exercises in Visual Thinking, published by the Mathematical Association of America Nota de contenido: Definitions and Basic Properties -- Methods of Constructing Copulas -- Archimedean Copulas -- Dependence -- Additional Topics En línea: http://dx.doi.org/10.1007/0-387-28678-0 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34753 An Introduction to Copulas [documento electrónico] / Nelsen, Roger B ; SpringerLink (Online service) . - New York, NY : Springer New York, 2006 . - XIV, 272 p : online resource. - (Springer Series in Statistics, ISSN 0172-7397) .
ISBN : 978-0-387-28678-5
Idioma : Inglés (eng)
Palabras clave: Mathematics Computer simulation Economics, Mathematical Probabilities Statistics Probability Theory and Stochastic Processes Statistical Methods for Business/Economics/Mathematical Finance/Insurance Quantitative Finance Simulation Modeling Clasificación: 51 Matemáticas Resumen: Copulas are functions that join multivariate distribution functions to their one-dimensional margins. The study of copulas and their role in statistics is a new but vigorously growing field. In this book the student or practitioner of statistics and probability will find discussions of the fundamental properties of copulas and some of their primary applications. The applications include the study of dependence and measures of association, and the construction of families of bivariate distributions. With 116 examples, 54 figures, and 167 exercises, this book is suitable as a text or for self-study. The only prerequisite is an upper level undergraduate course in probability and mathematical statistics, although some familiarity with nonparametric statistics would be useful. Knowledge of measure-theoretic probability is not required. The revised second edition includes new sections on extreme value copulas, tail dependence, and quasi-copulas. Roger B. Nelsen is Professor of Mathematics at Lewis & Clark College in Portland, Oregon. He is also the author of Proofs Without Words: Exercises in Visual Thinking and Proofs Without Words II: More Exercises in Visual Thinking, published by the Mathematical Association of America Nota de contenido: Definitions and Basic Properties -- Methods of Constructing Copulas -- Archimedean Copulas -- Dependence -- Additional Topics En línea: http://dx.doi.org/10.1007/0-387-28678-0 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34753 Ejemplares
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Título : Bayesian and Frequentist Regression Methods Tipo de documento: documento electrónico Autores: Wakefield, Jon ; SpringerLink (Online service) Editorial: New York, NY : Springer New York Fecha de publicación: 2013 Otro editor: Imprint: Springer Colección: Springer Series in Statistics, ISSN 0172-7397 Número de páginas: XIX, 697 p. 140 illus., 6 illus. in color Il.: online resource ISBN/ISSN/DL: 978-1-4419-0925-1 Idioma : Inglés (eng) Palabras clave: Statistics Statistical Theory and Methods Statistics, general Clasificación: 51 Matemáticas Resumen: Bayesian and Frequentist Regression Methods provides a modern account of both Bayesian and frequentist methods of regression analysis. Many texts cover one or the other of the approaches, but this is the most comprehensive combination of Bayesian and frequentist methods that exists in one place. The two philosophical approaches to regression methodology are featured here as complementary techniques, with theory and data analysis providing supplementary components of the discussion. In particular, methods are illustrated using a variety of data sets. The majority of the data sets are drawn from biostatistics but the techniques are generalizable to a wide range of other disciplines. While the philosophy behind each approach is discussed, the book is not ideological in nature and an emphasis is placed on practical application. It is shown that, in many situations, careful application of the respective approaches can lead to broadly similar conclusions. To use this text, the reader requires a basic understanding of calculus and linear algebra, and introductory courses in probability and statistical theory. The book is based on the author's experience teaching a graduate sequence in regression methods. The book website contains all of the code to reproduce all of the analyses and figures contained in the book Nota de contenido: Introduction -- Frequentist Inference -- Bayesian Inference -- Linear Models -- Binary Data Models -- General Regression Models En línea: http://dx.doi.org/10.1007/978-1-4419-0925-1 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=32184 Bayesian and Frequentist Regression Methods [documento electrónico] / Wakefield, Jon ; SpringerLink (Online service) . - New York, NY : Springer New York : Imprint: Springer, 2013 . - XIX, 697 p. 140 illus., 6 illus. in color : online resource. - (Springer Series in Statistics, ISSN 0172-7397) .
ISBN : 978-1-4419-0925-1
Idioma : Inglés (eng)
Palabras clave: Statistics Statistical Theory and Methods Statistics, general Clasificación: 51 Matemáticas Resumen: Bayesian and Frequentist Regression Methods provides a modern account of both Bayesian and frequentist methods of regression analysis. Many texts cover one or the other of the approaches, but this is the most comprehensive combination of Bayesian and frequentist methods that exists in one place. The two philosophical approaches to regression methodology are featured here as complementary techniques, with theory and data analysis providing supplementary components of the discussion. In particular, methods are illustrated using a variety of data sets. The majority of the data sets are drawn from biostatistics but the techniques are generalizable to a wide range of other disciplines. While the philosophy behind each approach is discussed, the book is not ideological in nature and an emphasis is placed on practical application. It is shown that, in many situations, careful application of the respective approaches can lead to broadly similar conclusions. To use this text, the reader requires a basic understanding of calculus and linear algebra, and introductory courses in probability and statistical theory. The book is based on the author's experience teaching a graduate sequence in regression methods. The book website contains all of the code to reproduce all of the analyses and figures contained in the book Nota de contenido: Introduction -- Frequentist Inference -- Bayesian Inference -- Linear Models -- Binary Data Models -- General Regression Models En línea: http://dx.doi.org/10.1007/978-1-4419-0925-1 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=32184 Ejemplares
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Título : Bayesian Reliability Tipo de documento: documento electrónico Autores: Hamada, Michael S ; SpringerLink (Online service) ; Wilson, Alyson G ; Reese, C. Shane ; Martz, Harry F Editorial: New York, NY : Springer New York Fecha de publicación: 2008 Colección: Springer Series in Statistics, ISSN 0172-7397 Número de páginas: XVI, 436 p Il.: online resource ISBN/ISSN/DL: 978-0-387-77950-8 Idioma : Inglés (eng) Palabras clave: Mathematics Probabilities Statistics Quality control Reliability Industrial safety Probability Theory and Stochastic Processes for Engineering, Physics, Computer Science, Chemistry Earth Sciences Control, Reliability, Safety Risk Statistical Methods Clasificación: 51 Matemáticas Resumen: Bayesian Reliability presents modern methods and techniques for analyzing reliability data from a Bayesian perspective. The adoption and application of Bayesian methods in virtually all branches of science and engineering have significantly increased over the past few decades. This increase is largely due to advances in simulation-based computational tools for implementing Bayesian methods. The authors extensively use such tools throughout this book, focusing on assessing the reliability of components and systems with particular attention to hierarchical models and models incorporating explanatory variables. Such models include failure time regression models, accelerated testing models, and degradation models. The authors pay special attention to Bayesian goodness-of-fit testing, model validation, reliability test design, and assurance test planning. Throughout the book, the authors use Markov chain Monte Carlo (MCMC) algorithms for implementing Bayesian analyses--algorithms that make the Bayesian approach to reliability computationally feasible and conceptually straightforward. This book is primarily a reference collection of modern Bayesian methods in reliability for use by reliability practitioners. There are more than 70 illustrative examples, most of which utilize real-world data. This book can also be used as a textbook for a course in reliability and contains more than 160 exercises. Noteworthy highlights of the book include Bayesian approaches for the following: Goodness-of-fit and model selection methods Hierarchical models for reliability estimation Fault tree analysis methodology that supports data acquisition at all levels in the tree Bayesian networks in reliability analysis Analysis of failure count and failure time data collected from repairable systems, and the assessment of various related performance criteria Nota de contenido: Reliability Concepts -- Bayesian Inference -- Advanced Bayesian Modeling and Computational Methods -- Component Reliability -- System Reliability -- Repairable System Reliability -- Regression Models in Reliability -- Using Degradation Data to Assess Reliability -- Planning for Reliability Data Collection -- Assurance Testing En línea: http://dx.doi.org/10.1007/978-0-387-77950-8 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34240 Bayesian Reliability [documento electrónico] / Hamada, Michael S ; SpringerLink (Online service) ; Wilson, Alyson G ; Reese, C. Shane ; Martz, Harry F . - New York, NY : Springer New York, 2008 . - XVI, 436 p : online resource. - (Springer Series in Statistics, ISSN 0172-7397) .
ISBN : 978-0-387-77950-8
Idioma : Inglés (eng)
Palabras clave: Mathematics Probabilities Statistics Quality control Reliability Industrial safety Probability Theory and Stochastic Processes for Engineering, Physics, Computer Science, Chemistry Earth Sciences Control, Reliability, Safety Risk Statistical Methods Clasificación: 51 Matemáticas Resumen: Bayesian Reliability presents modern methods and techniques for analyzing reliability data from a Bayesian perspective. The adoption and application of Bayesian methods in virtually all branches of science and engineering have significantly increased over the past few decades. This increase is largely due to advances in simulation-based computational tools for implementing Bayesian methods. The authors extensively use such tools throughout this book, focusing on assessing the reliability of components and systems with particular attention to hierarchical models and models incorporating explanatory variables. Such models include failure time regression models, accelerated testing models, and degradation models. The authors pay special attention to Bayesian goodness-of-fit testing, model validation, reliability test design, and assurance test planning. Throughout the book, the authors use Markov chain Monte Carlo (MCMC) algorithms for implementing Bayesian analyses--algorithms that make the Bayesian approach to reliability computationally feasible and conceptually straightforward. This book is primarily a reference collection of modern Bayesian methods in reliability for use by reliability practitioners. There are more than 70 illustrative examples, most of which utilize real-world data. This book can also be used as a textbook for a course in reliability and contains more than 160 exercises. Noteworthy highlights of the book include Bayesian approaches for the following: Goodness-of-fit and model selection methods Hierarchical models for reliability estimation Fault tree analysis methodology that supports data acquisition at all levels in the tree Bayesian networks in reliability analysis Analysis of failure count and failure time data collected from repairable systems, and the assessment of various related performance criteria Nota de contenido: Reliability Concepts -- Bayesian Inference -- Advanced Bayesian Modeling and Computational Methods -- Component Reliability -- System Reliability -- Repairable System Reliability -- Regression Models in Reliability -- Using Degradation Data to Assess Reliability -- Planning for Reliability Data Collection -- Assurance Testing En línea: http://dx.doi.org/10.1007/978-0-387-77950-8 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34240 Ejemplares
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Título : Comparing Distributions Tipo de documento: documento electrónico Autores: Thas, Olivier ; SpringerLink (Online service) Editorial: New York, NY : Springer New York Fecha de publicación: 2010 Otro editor: Imprint: Springer Colección: Springer Series in Statistics, ISSN 0172-7397 Número de páginas: XVI, 354 p Il.: online resource ISBN/ISSN/DL: 978-0-387-92710-7 Idioma : Inglés (eng) Palabras clave: Statistics Operations research Decision making Data mining Biostatistics Probabilities Social sciences Statistics, general Probability Theory and Stochastic Processes Methodology of the Sciences Mining Knowledge Discovery Operation Research/Decision Clasificación: 51 Matemáticas Resumen: Comparing Distributions refers to the statistical data analysis that encompasses the traditional goodness-of-fit testing. Whereas the latter includes only formal statistical hypothesis tests for the one-sample and the K-sample problems, this book presents a more general and informative treatment by also considering graphical and estimation methods. A procedure is said to be informative when it provides information on the reason for rejecting the null hypothesis. Despite the historically seemingly different development of methods, this book emphasises the similarities between the methods by linking them to a common theory backbone. This book consists of two parts. In the first part statistical methods for the one-sample problem are discussed. The second part of the book treats the K-sample problem. Many sections of this second part of the book may be of interest to every statistician who is involved in comparative studies. The book gives a self-contained theoretical treatment of a wide range of goodness-of-fit methods, including graphical methods, hypothesis tests, model selection and density estimation. It relies on parametric, semiparametric and nonparametric theory, which is kept at an intermediate level; the intuition and heuristics behind the methods are usually provided as well. The book contains many data examples that are analysed with the cd R-package that is written by the author. All examples include the R-code. Because many methods described in this book belong to the basic toolbox of almost every statistician, the book should be of interest to a wide audience. In particular, the book may be useful for researchers, graduate students and PhD students who need a starting point for doing research in the area of goodness-of-fit testing. Practitioners and applied statisticians may also be interested because of the many examples, the R-code and the stress on the informative nature of the procedures. Olivier Thas is Associate Professor of Biostatistics at Ghent University. He has published methodological papers on goodness-of-fit testing, but he has also published more applied work in the areas of environmental statistics and genomics Nota de contenido: One-Sample Problems -- Preliminaries (Building Blocks) -- Graphical Tools -- Smooth Tests -- Methods Based on the Empirical Distribution Function -- Two-Sample and K-Sample Problems -- Preliminaries (Building Blocks) -- Graphical Tools -- Some Important Two-Sample Tests -- Smooth Tests -- Methods Based on the Empirical Distribution Function -- Two Final Methods and Some Final Thoughts En línea: http://dx.doi.org/10.1007/978-0-387-92710-7 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33525 Comparing Distributions [documento electrónico] / Thas, Olivier ; SpringerLink (Online service) . - New York, NY : Springer New York : Imprint: Springer, 2010 . - XVI, 354 p : online resource. - (Springer Series in Statistics, ISSN 0172-7397) .
ISBN : 978-0-387-92710-7
Idioma : Inglés (eng)
Palabras clave: Statistics Operations research Decision making Data mining Biostatistics Probabilities Social sciences Statistics, general Probability Theory and Stochastic Processes Methodology of the Sciences Mining Knowledge Discovery Operation Research/Decision Clasificación: 51 Matemáticas Resumen: Comparing Distributions refers to the statistical data analysis that encompasses the traditional goodness-of-fit testing. Whereas the latter includes only formal statistical hypothesis tests for the one-sample and the K-sample problems, this book presents a more general and informative treatment by also considering graphical and estimation methods. A procedure is said to be informative when it provides information on the reason for rejecting the null hypothesis. Despite the historically seemingly different development of methods, this book emphasises the similarities between the methods by linking them to a common theory backbone. This book consists of two parts. In the first part statistical methods for the one-sample problem are discussed. The second part of the book treats the K-sample problem. Many sections of this second part of the book may be of interest to every statistician who is involved in comparative studies. The book gives a self-contained theoretical treatment of a wide range of goodness-of-fit methods, including graphical methods, hypothesis tests, model selection and density estimation. It relies on parametric, semiparametric and nonparametric theory, which is kept at an intermediate level; the intuition and heuristics behind the methods are usually provided as well. The book contains many data examples that are analysed with the cd R-package that is written by the author. All examples include the R-code. Because many methods described in this book belong to the basic toolbox of almost every statistician, the book should be of interest to a wide audience. In particular, the book may be useful for researchers, graduate students and PhD students who need a starting point for doing research in the area of goodness-of-fit testing. Practitioners and applied statisticians may also be interested because of the many examples, the R-code and the stress on the informative nature of the procedures. Olivier Thas is Associate Professor of Biostatistics at Ghent University. He has published methodological papers on goodness-of-fit testing, but he has also published more applied work in the areas of environmental statistics and genomics Nota de contenido: One-Sample Problems -- Preliminaries (Building Blocks) -- Graphical Tools -- Smooth Tests -- Methods Based on the Empirical Distribution Function -- Two-Sample and K-Sample Problems -- Preliminaries (Building Blocks) -- Graphical Tools -- Some Important Two-Sample Tests -- Smooth Tests -- Methods Based on the Empirical Distribution Function -- Two Final Methods and Some Final Thoughts En línea: http://dx.doi.org/10.1007/978-0-387-92710-7 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33525 Ejemplares
Signatura Medio Ubicación Sub-localización Sección Estado ningún ejemplar A Comparison of the Bayesian and Frequentist Approaches to Estimation / Samaniego, Francisco J (2010)
Título : A Comparison of the Bayesian and Frequentist Approaches to Estimation Tipo de documento: documento electrónico Autores: Samaniego, Francisco J ; SpringerLink (Online service) Editorial: New York, NY : Springer New York Fecha de publicación: 2010 Colección: Springer Series in Statistics, ISSN 0172-7397 Número de páginas: XIII, 225 p Il.: online resource ISBN/ISSN/DL: 978-1-4419-5941-6 Idioma : Inglés (eng) Palabras clave: Mathematics Probabilities Statistics Social sciences Probability Theory and Stochastic Processes Statistical Methods Methodology of the Sciences Clasificación: 51 Matemáticas Resumen: This monograph contributes to the area of comparative statistical inference. Attention is restricted to the important subfield of statistical estimation. The book is intended for an audience having a solid grounding in probability and statistics at the level of the year-long undergraduate course taken by statistics and mathematics majors. The necessary background on Decision Theory and the frequentist and Bayesian approaches to estimation is presented and carefully discussed in Chapters 1–3. The “threshold problem” -- identifying the boundary between Bayes estimators which tend to outperform standard frequentist estimators and Bayes estimators which don’t -- is formulated in an analytically tractable way in Chapter 4. The formulation includes a specific (decision-theory based) criterion for comparing estimators. The centerpiece of the monograph is Chapter 5 in which, under quite general conditions, an explicit solution to the threshold is obtained for the problem of estimating a scalar parameter under squared error loss. The six chapters that follow address a variety of other contexts in which the threshold problem can be productively treated. Included are treatments of the Bayesian consensus problem, the threshold problem for estimation problems involving of multi-dimensional parameters and/or asymmetric loss, the estimation of nonidentifiable parameters, empirical Bayes methods for combining data from ‘similar’ experiments and linear Bayes methods for combining data from ‘related’ experiments. The final chapter provides an overview of the monograph’s highlights and a discussion of areas and problems in need of further research. F. J. Samaniego is a Distinguished Professor of Statistics at the University of California, Davis. He served as Theory and Methods Editor of the Journal of the American Statistical Association (2003-05), was the 2004 recipient of the Davis Prize for Undergraduate Teaching and Scholarly Achievement, and is an elected Fellow of the ASA, the IMS and the RSS and an elected Member of the ISI Nota de contenido: Point Estimation from a Decision-Theoretic Viewpoint -- An Overview of the Frequentist Approach to Estimation -- An Overview of the Bayesian Approach to Estimation -- The Threshold Problem -- Comparing Bayesian and Frequentist Estimators of a Scalar Parameter -- Conjugacy, Self-Consistency and Bayesian Consensus -- Bayesian vs. Frequentist Shrinkage in Multivariate Normal Problems -- Comparing Bayesian and Frequentist Estimators under Asymmetric Loss -- The Treatment of Nonidentifiable Models -- Improving on Standard Bayesian and Frequentist Estimators -- Combining Data from “Related” Experiments -- Fatherly Advice En línea: http://dx.doi.org/10.1007/978-1-4419-5941-6 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33623 A Comparison of the Bayesian and Frequentist Approaches to Estimation [documento electrónico] / Samaniego, Francisco J ; SpringerLink (Online service) . - New York, NY : Springer New York, 2010 . - XIII, 225 p : online resource. - (Springer Series in Statistics, ISSN 0172-7397) .
ISBN : 978-1-4419-5941-6
Idioma : Inglés (eng)
Palabras clave: Mathematics Probabilities Statistics Social sciences Probability Theory and Stochastic Processes Statistical Methods Methodology of the Sciences Clasificación: 51 Matemáticas Resumen: This monograph contributes to the area of comparative statistical inference. Attention is restricted to the important subfield of statistical estimation. The book is intended for an audience having a solid grounding in probability and statistics at the level of the year-long undergraduate course taken by statistics and mathematics majors. The necessary background on Decision Theory and the frequentist and Bayesian approaches to estimation is presented and carefully discussed in Chapters 1–3. The “threshold problem” -- identifying the boundary between Bayes estimators which tend to outperform standard frequentist estimators and Bayes estimators which don’t -- is formulated in an analytically tractable way in Chapter 4. The formulation includes a specific (decision-theory based) criterion for comparing estimators. The centerpiece of the monograph is Chapter 5 in which, under quite general conditions, an explicit solution to the threshold is obtained for the problem of estimating a scalar parameter under squared error loss. The six chapters that follow address a variety of other contexts in which the threshold problem can be productively treated. Included are treatments of the Bayesian consensus problem, the threshold problem for estimation problems involving of multi-dimensional parameters and/or asymmetric loss, the estimation of nonidentifiable parameters, empirical Bayes methods for combining data from ‘similar’ experiments and linear Bayes methods for combining data from ‘related’ experiments. The final chapter provides an overview of the monograph’s highlights and a discussion of areas and problems in need of further research. F. J. Samaniego is a Distinguished Professor of Statistics at the University of California, Davis. He served as Theory and Methods Editor of the Journal of the American Statistical Association (2003-05), was the 2004 recipient of the Davis Prize for Undergraduate Teaching and Scholarly Achievement, and is an elected Fellow of the ASA, the IMS and the RSS and an elected Member of the ISI Nota de contenido: Point Estimation from a Decision-Theoretic Viewpoint -- An Overview of the Frequentist Approach to Estimation -- An Overview of the Bayesian Approach to Estimation -- The Threshold Problem -- Comparing Bayesian and Frequentist Estimators of a Scalar Parameter -- Conjugacy, Self-Consistency and Bayesian Consensus -- Bayesian vs. Frequentist Shrinkage in Multivariate Normal Problems -- Comparing Bayesian and Frequentist Estimators under Asymmetric Loss -- The Treatment of Nonidentifiable Models -- Improving on Standard Bayesian and Frequentist Estimators -- Combining Data from “Related” Experiments -- Fatherly Advice En línea: http://dx.doi.org/10.1007/978-1-4419-5941-6 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33623 Ejemplares
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